CVPR Buzz

Hieu Pham, Zihang Dai, Qizhe Xie, Quoc V. Le

We present Meta Pseudo Labels, a semi-supervised learning method that achieves a new state-of-the-art top-1 accuracy of 90.2% on ImageNet, which is 1.6% better than the existing state-of-the-art. [Expand]

Thursday Poster Session

Aleksander Holynski, Brian L. Curless, Steven M. Seitz, Richard Szeliski

In this paper, we demonstrate a fully automatic method for converting a still image into a realistic animated looping video. [Expand]

Tuesday Poster Session

Patrick Esser, Robin Rombach, Bjorn Ommer

Designed to learn long-range interactions on sequential data, transformers continue to show state-of-the-art results on a wide variety of tasks. [Expand]

Thursday Poster Session

Shanchuan Lin, Andrey Ryabtsev, Soumyadip Sengupta, Brian L. Curless, Steven M. Seitz, Ira Kemelmacher-Shlizerman

We introduce a real-time, high-resolution background replacement technique which operates at 30fps in 4K resolution, and 60fps for HD on a modern GPU. [Expand]

Wednesday Poster Session

Xiaohan Ding, Xiangyu Zhang, Ningning Ma, Jungong Han, Guiguang Ding, Jian Sun

We present a simple but powerful architecture of convolutional neural network, which has a VGG-like inference-time body composed of nothing but a stack of 3x3 convolution and ReLU, while the training-time model has a multi-branch topology. [Expand]

Thursday Poster Session

Dan Hendrycks, Kevin Zhao, Steven Basart, Jacob Steinhardt, Dawn Song

We introduce two challenging datasets that reliably cause machine learning model performance to substantially degrade. [Expand]

Thursday Poster Session

Karan Desai, Justin Johnson

The de-facto approach to many vision tasks is to start from pretrained visual representations, typically learned via supervised training on ImageNet. [Expand]

Wednesday Poster Session

Ting-Chun Wang, Arun Mallya, Ming-Yu Liu

We propose a neural talking-head video synthesis model and demonstrate its application to video conferencing. [Expand]

Wednesday Poster Session

Yinbo Chen, Sifei Liu, Xiaolong Wang

How to represent an image? While the visual world is presented in a continuous manner, machines store and see the images in a discrete way with 2D arrays of pixels. [Expand]

Wednesday Poster Session

Pradyumna Reddy, Michael Gharbi, Michal Lukac, Niloy J. Mitra

Vector graphics are widely used to represent fonts, logos, digital artworks, and graphic designs. [Expand]

Wednesday Poster Session

Xinlei Chen, Kaiming He

Siamese networks have become a common structure in various recent models for unsupervised visual representation learning. [Expand]

Friday Poster Session

Aravind Srinivas, Tsung-Yi Lin, Niki Parmar, Jonathon Shlens, Pieter Abbeel, Ashish Vaswani

We present BoTNet, a conceptually simple yet powerful backbone architecture that incorporates self-attention for multiple computer vision tasks including image classification, object detection and instance segmentation. [Expand]

Friday Poster Session

Duo Li, Jie Hu, Changhu Wang, Xiangtai Li, Qi She, Lei Zhu, Tong Zhang, Qifeng Chen

Convolution has been the core ingredient of modern neural networks, triggering the surge of deep learning in vision. [Expand]

Thursday Poster Session

Golnaz Ghiasi, Yin Cui, Aravind Srinivas, Rui Qian, Tsung-Yi Lin, Ekin D. Cubuk, Quoc V. Le, Barret Zoph

Building instance segmentation models that are data-efficient and can handle rare object categories is an important challenge in computer vision. [Expand]

Tuesday Poster Session

Ricardo Martin-Brualla, Noha Radwan, Mehdi S. M. Sajjadi, Jonathan T. Barron, Alexey Dosovitskiy, Daniel Duckworth

We present a learning-based method for synthesizingnovel views of complex scenes using only unstructured collections of in-the-wild photographs. [Expand]

Wednesday Poster Session

S. Mahdi H. Miangoleh, Sebastian Dille, Long Mai, Sylvain Paris, Yagiz Aksoy

Neural networks have shown great abilities in estimating depth from a single image. [Expand]

Wednesday Poster Session

Johannes Kopf, Xuejian Rong, Jia-Bin Huang

We present an algorithm for estimating consistent dense depth maps and camera poses from a monocular video. [Expand]

Monday Poster Session

Suttisak Wizadwongsa, Pakkapon Phongthawee, Jiraphon Yenphraphai, Supasorn Suwajanakorn

We present NeX, a new approach to novel view synthesis based on enhancements of multiplane image (MPI) that can reproduce next-level view-dependent effects--in real time. [Expand]

Wednesday Poster Session

Aliaksandr Siarohin, Oliver J. Woodford, Jian Ren, Menglei Chai, Sergey Tulyakov

We propose novel motion representations for animating articulated objects consisting of distinct parts. [Expand]

Thursday Poster Session

Erika Lu, Forrester Cole, Tali Dekel, Andrew Zisserman, William T. Freeman, Michael Rubinstein

Computer vision has become increasingly better at segmenting objects in images and videos; however, scene effects related to the objects -- shadows, reflections, generated smoke, etc. [Expand]

Tuesday Poster Session

Yujun Shen, Bolei Zhou

A rich set of interpretable dimensions has been shown to emerge in the latent space of the Generative Adversarial Networks (GANs) trained for synthesizing images. [Expand]

Monday Poster Session

Jiayan Qiu, Yiding Yang, Xinchao Wang, Dacheng Tao

What scene elements, if any, are indispensable for recognizing a scene? We strive to answer this question through the lens of an end-to-end learning scheme. [Expand]

Wednesday Poster Session

Towaki Takikawa, Joey Litalien, Kangxue Yin, Karsten Kreis, Charles Loop, Derek Nowrouzezahrai, Alec Jacobson, Morgan McGuire, Sanja Fidler

Neural signed distance functions (SDFs) are emerging as an effective representation for 3D shapes. [Expand]

Thursday Poster Session

Paul-Edouard Sarlin, Ajaykumar Unagar, Mans Larsson, Hugo Germain, Carl Toft, Viktor Larsson, Marc Pollefeys, Vincent Lepetit, Lars Hammarstrand, Fredrik Kahl, Torsten Sattler

Camera pose estimation in known scenes is a 3D geometry task recently tackled by multiple learning algorithms. [Expand]

Tuesday Poster Session

Cheng Zhang, Zhaopeng Cui, Yinda Zhang, Bing Zeng, Marc Pollefeys, Shuaicheng Liu

We present a new pipeline for holistic 3D scene understanding from a single image, which could predict object shape, object pose and scene layout. [Expand]

Wednesday Poster Session

Hanting Chen, Yunhe Wang, Tianyu Guo, Chang Xu, Yiping Deng, Zhenhua Liu, Siwei Ma, Chunjing Xu, Chao Xu, Wen Gao

As the computing power of modern hardware is increasing strongly, pre-trained deep learning models (e.g., BERT, GPT-3) learned on large-scale datasets have shown their effectiveness over conventional methods. [Expand]

Thursday Poster Session

Zhengxia Zou, Tianyang Shi, Shuang Qiu, Yi Yuan, Zhenwei Shi

This paper proposes an image-to-painting translation method that generates vivid and realistic painting artworks with controllable styles. [Expand]

Friday Poster Session

Panos Achlioptas, Maks Ovsjanikov, Kilichbek Haydarov, Mohamed Elhoseiny, Leonidas J. Guibas

We present a novel large-scale dataset and accompanying machine learning models aimed at providing a detailed understanding of the interplay between visual content, its emotional effect, and explanations for the latter in language. [Expand]

Thursday Poster Session

Yuxuan Zhang, Huan Ling, Jun Gao, Kangxue Yin, Jean-Francois Lafleche, Adela Barriuso, Antonio Torralba, Sanja Fidler

We introduce DatasetGAN: an automatic procedure to generate massive datasets of high-quality semantically segmented images requiring minimal human effort. [Expand]

Wednesday Poster Session

Chun-Liang Li, Kihyuk Sohn, Jinsung Yoon, Tomas Pfister

We aim at constructing a high performance model for defect detection that detects unknown anomalous patterns of an image without anomalous data. [Expand]

Wednesday Poster Session

Seung Wook Kim, Jonah Philion, Antonio Torralba, Sanja Fidler

Realistic simulators are critical for training and verifying robotics systems. [Expand]

Tuesday Poster Session

Zhengqi Li, Simon Niklaus, Noah Snavely, Oliver Wang

We present a method to perform novel view and time synthesis of dynamic scenes, requiring only a monocular video with known camera poses as input. [Expand]

Tuesday Poster Session

Tao Yang, Peiran Ren, Xuansong Xie, Lei Zhang

Blind face restoration (BFR) from severely degraded face images in the wild is a very challenging problem. [Expand]

Monday Poster Session

Ivan Anokhin, Kirill Demochkin, Taras Khakhulin, Gleb Sterkin, Victor Lempitsky, Denis Korzhenkov

Existing image generator networks rely heavily on spatial convolutions and, optionally, self-attention blocks in order to gradually synthesize images in a coarse-to-fine manner. [Expand]

Thursday Poster Session

Daiqing Li, Junlin Yang, Karsten Kreis, Antonio Torralba, Sanja Fidler

Training deep networks with limited labeled data while achieving a strong generalization ability is key in the quest to reduce human annotation efforts. [Expand]

Wednesday Poster Session

Ashish Vaswani, Prajit Ramachandran, Aravind Srinivas, Niki Parmar, Blake Hechtman, Jonathon Shlens

Self-attention has the promise of improving computer vision systems due to parameter-independent scaling of receptive fields and content-dependent interactions, in contrast to parameter-dependent scaling and content-independent interactions of convolutions. [Expand]

Thursday Poster Session

Elad Richardson, Yuval Alaluf, Or Patashnik, Yotam Nitzan, Yaniv Azar, Stav Shapiro, Daniel Cohen-Or

We present a generic image-to-image translation framework, pixel2style2pixel (pSp). [Expand]

Monday Poster Session

Sixiao Zheng, Jiachen Lu, Hengshuang Zhao, Xiatian Zhu, Zekun Luo, Yabiao Wang, Yanwei Fu, Jianfeng Feng, Tao Xiang, Philip H.S. Torr, Li Zhang

Most recent semantic segmentation methods adopt a fully-convolutional network (FCN) with an encoder-decoder architecture. [Expand]

Tuesday Poster Session

Felix Wimbauer, Nan Yang, Lukas von Stumberg, Niclas Zeller, Daniel Cremers

In this paper, we propose MonoRec, a semi-supervised monocular dense reconstruction architecture that predicts depth maps from a single moving camera in dynamic environments. [Expand]

Tuesday Poster Session

Pedro Savarese, Sunnie S. Y. Kim, Michael Maire, Greg Shakhnarovich, David McAllester

We study image segmentation from an information-theoretic perspective, proposing a novel adversarial method that performs unsupervised segmentation by partitioning images into maximally independent sets. [Expand]

Tuesday Poster Session

Qianqian Wang, Zhicheng Wang, Kyle Genova, Pratul P. Srinivasan, Howard Zhou, Jonathan T. Barron, Ricardo Martin-Brualla, Noah Snavely, Thomas Funkhouser

We present a method that synthesizes novel views of complex scenes by interpolating a sparse set of nearby views. [Expand]

Tuesday Poster Session

Josip Djolonga, Jessica Yung, Michael Tschannen, Rob Romijnders, Lucas Beyer, Alexander Kolesnikov, Joan Puigcerver, Matthias Minderer, Alexander D'Amour, Dan Moldovan, Sylvain Gelly, Neil Houlsby, Xiaohua Zhai, Mario Lucic

Modern deep convolutional networks (CNNs) are often criticized for not generalizing under distributional shifts. [Expand]

Friday Poster Session

Jiaming Sun, Zehong Shen, Yuang Wang, Hujun Bao, Xiaowei Zhou

We present a novel method for local image feature matching. [Expand]

Wednesday Poster Session

Brett D. Roads, Bradley C. Love

Advances in supervised learning approaches to object recognition flourished in part because of the availability of high-quality datasets and associated benchmarks. [Expand]

Tuesday Poster Session

Daniel Lichy, Jiaye Wu, Soumyadip Sengupta, David W. Jacobs

In this paper, we present a technique for estimating the geometry and reflectance of objects using only a camera, flashlight, and optionally a tripod. [Expand]

Tuesday Poster Session

Sangdoo Yun, Seong Joon Oh, Byeongho Heo, Dongyoon Han, Junsuk Choe, Sanghyuk Chun

ImageNet has been the most popular image classification benchmark, but it is also the one with a significant level of label noise. [Expand]

Monday Poster Session

Jiaming Sun, Yiming Xie, Linghao Chen, Xiaowei Zhou, Hujun Bao

We present a novel framework named NeuralRecon for real-time 3D scene reconstruction from a monocular video. [Expand]

Friday Poster Session

Li Siyao, Shiyu Zhao, Weijiang Yu, Wenxiu Sun, Dimitris Metaxas, Chen Change Loy, Ziwei Liu

In the animation industry, cartoon videos are usually produced at low frame rate since hand drawing of such frames is costly and time-consuming. [Expand]

Tuesday Poster Session

Alex Yu, Vickie Ye, Matthew Tancik, Angjoo Kanazawa

We propose pixelNeRF, a learning framework that predicts a continuous neural scene representation conditioned on one or few input images. [Expand]

Tuesday Poster Session

Zhigang Dai, Bolun Cai, Yugeng Lin, Junying Chen

Object detection with transformers (DETR) reaches competitive performance with Faster R-CNN via a transformer encoder-decoder architecture. [Expand]

Monday Poster Session

Jie Lei, Linjie Li, Luowei Zhou, Zhe Gan, Tamara L. Berg, Mohit Bansal, Jingjing Liu

The canonical approach to video-and-language learning (e.g., video question answering) dictates a neural model to learn from offline-extracted dense video features from vision models and text features from language models. [Expand]

Wednesday Poster Session

Willi Menapace, Stephane Lathuiliere, Sergey Tulyakov, Aliaksandr Siarohin, Elisa Ricci

This paper introduces the unsupervised learning problem of playable video generation (PVG). [Expand]

Wednesday Poster Session

David B. Lindell, Julien N. P. Martel, Gordon Wetzstein

Numerical integration is a foundational technique in scientific computing and is at the core of many computer vision applications. [Expand]

Thursday Poster Session

Gernot Riegler, Vladlen Koltun

We present Stable View Synthesis (SVS). [Expand]

Thursday Poster Session

Yufei Ye, Shubham Tulsiani, Abhinav Gupta

We aim to infer 3D shape and pose of objects from a single image and propose a learning-based approach that can train from unstructured image collections, using only segmentation outputs from off-the-shelf recognition systems as supervisory signal (i.e. [Expand]

Wednesday Poster Session

Sida Peng, Yuanqing Zhang, Yinghao Xu, Qianqian Wang, Qing Shuai, Hujun Bao, Xiaowei Zhou

This paper addresses the challenge of novel view synthesis for a human performer from a very sparse set of camera views. [Expand]

Wednesday Poster Session

Anton Cherepkov, Andrey Voynov, Artem Babenko

Generative Adversarial Networks (GANs) are currently an indispensable tool for visual editing, being a standard component of image-to-image translation and image restoration pipelines. [Expand]

Tuesday Poster Session

Amirhossein Habibian, Davide Abati, Taco S. Cohen, Babak Ehteshami Bejnordi

We propose Skip-Convolutions to leverage the large amount of redundancies in video streams and save computations. [Expand]

Monday Poster Session

Jamie Watson, Oisin Mac Aodha, Victor Prisacariu, Gabriel Brostow, Michael Firman

Self-supervised monocular depth estimation networks are trained to predict scene depth using nearby frames as a supervision signal during training. [Expand]

Monday Poster Session

Ho Kei Cheng, Yu-Wing Tai, Chi-Keung Tang

We present Modular interactive VOS (MiVOS) framework which decouples interaction-to-mask and mask propagation, allowing for higher generalizability and better performance. [Expand]

Tuesday Poster Session

Bing Shuai, Andrew Berneshawi, Xinyu Li, Davide Modolo, Joseph Tighe

In this work, we focus on improving online multi-object tracking (MOT). [Expand]

Thursday Poster Session

Heng Yang, Wei Dong, Luca Carlone, Vladlen Koltun

We present self-supervised geometric perception (SGP), the first general framework to learn a feature descriptor for correspondence matching without any ground-truth geometric model labels (e.g., camera poses, rigid transformations). [Expand]

Thursday Poster Session

Michael Dorkenwald, Timo Milbich, Andreas Blattmann, Robin Rombach, Konstantinos G. Derpanis, Bjorn Ommer

Video understanding calls for a model to learn the characteristic interplay between static scene content and its dynamics: Given an image, the model must be able to predict a future progression of the portrayed scene and, conversely, a video should be explained in terms of its static image content and all the remaining characteristics not present in the initial frame. [Expand]

Tuesday Poster Session

Tamar Rott Shaham, Michael Gharbi, Richard Zhang, Eli Shechtman, Tomer Michaeli

We introduce a new generator architecture, aimed at fast and efficient high-resolution image-to-image translation. [Expand]

Thursday Poster Session

Wenqi Xian, Jia-Bin Huang, Johannes Kopf, Changil Kim

We present a method that learns a spatiotemporal neural irradiance field for dynamic scenes from a single video. [Expand]

Wednesday Poster Session

Enric Corona, Albert Pumarola, Guillem Alenya, Gerard Pons-Moll, Francesc Moreno-Noguer

In this paper we introduce SMPLicit, a novel generative model to jointly represent body pose, shape and clothing geometry. [Expand]

Thursday Poster Session

Yasamin Jafarian, Hyun Soo Park

A key challenge of learning the geometry of dressed humans lies in the limited availability of the ground truth data (e.g., 3D scanned models), which results in the performance degradation of 3D human reconstruction when applying to real world imagery. [Expand]

Thursday Poster Session

Yicheng Liu, Jinghuai Zhang, Liangji Fang, Qinhong Jiang, Bolei Zhou

Predicting multiple plausible future trajectories of the nearby vehicles is crucial for the safety of autonomous driving. [Expand]

Wednesday Poster Session

Aljaz Bozic, Pablo Palafox, Michael Zollhofer, Justus Thies, Angela Dai, Matthias Niessner

We introduce Neural Deformation Graphs for globally-consistent deformation tracking and 3D reconstruction of non-rigid objects. [Expand]

Monday Poster Session

Aditya Prakash, Kashyap Chitta, Andreas Geiger

How should representations from complementary sensors be integrated for autonomous driving? Geometry-based sensor fusion has shown great promise for perception tasks such as object detection and motion forecasting. [Expand]

Tuesday Poster Session

Yifan Xu, Weijian Xu, David Cheung, Zhuowen Tu

In this paper, we present a joint end-to-end line segment detection algorithm using Transformers that is post-processing and heuristics-guided intermediate processing (edge/junction/region detection) free. [Expand]

Tuesday Poster Session

Chengchao Shen, Youtan Yin, Xinchao Wang, Xubin Li, Jie Song, Mingli Song

Generative Adversarial Networks (GANs) have demonstrated unprecedented success in various image generation tasks. [Expand]

Tuesday Poster Session

Michael Niemeyer, Andreas Geiger

Deep generative models allow for photorealistic image synthesis at high resolutions. [Expand]

Thursday Poster Session

Rui Qian, Tianjian Meng, Boqing Gong, Ming-Hsuan Yang, Huisheng Wang, Serge Belongie, Yin Cui

We present a self-supervised Contrastive Video Representation Learning (CVRL) method to learn spatiotemporal visual representations from unlabeled videos. [Expand]

Tuesday Poster Session

Hila Chefer, Shir Gur, Lior Wolf

Self-attention techniques, and specifically Transformers, are dominating the field of text processing and are becoming increasingly popular in computer vision classification tasks. [Expand]

Monday Poster Session

Brendan Duke, Abdalla Ahmed, Christian Wolf, Parham Aarabi, Graham W. Taylor

In this paper we introduce a Transformer-based approach to video object segmentation (VOS). [Expand]

Tuesday Poster Session

Rui Xu, Xintao Wang, Kai Chen, Bolei Zhou, Chen Change Loy

SinGAN shows impressive capability in learning internal patch distribution despite its limited effective receptive field. [Expand]

Thursday Poster Session

Sanghyuk Chun, Seong Joon Oh, Rafael Sampaio de Rezende, Yannis Kalantidis, Diane Larlus

Cross-modal retrieval methods build a common representation space for samples from multiple modalities, typically from the vision and the language domains. [Expand]

Wednesday Poster Session

Gaurav Parmar, Dacheng Li, Kwonjoon Lee, Zhuowen Tu

We present a new generative autoencoder model with dual contradistinctive losses to improve generative autoencoder that performs simultaneous inference (reconstruction) and synthesis (sampling). [Expand]

Monday Poster Session

Albert Pumarola, Enric Corona, Gerard Pons-Moll, Francesc Moreno-Noguer

Neural rendering techniques combining machine learning with geometric reasoning have arisen as one of the most promising approaches for synthesizing novel views of a scene from a sparse set of images. [Expand]

Wednesday Poster Session

Boyi Li, Felix Wu, Ser-Nam Lim, Serge Belongie, Kilian Q. Weinberger

The moments (a.k.a., mean and standard deviation) of latent features are often removed as noise when training image recognition models, to increase stability and reduce training time. [Expand]

Thursday Poster Session

Guy Gafni, Justus Thies, Michael Zollhofer, Matthias Niessner

We present dynamic neural radiance fields for modeling the appearance and dynamics of a human face. [Expand]

Wednesday Poster Session

Qiang Chen, Yingming Wang, Tong Yang, Xiangyu Zhang, Jian Cheng, Jian Sun

This paper revisits feature pyramids networks (FPN) for one-stage detectors and points out that the success of FPN is due to its divide-and-conquer solution to the optimization problem in object detection rather than multi-scale feature fusion. [Expand]

Thursday Poster Session

Mandy Lu, Qingyu Zhao, Jiequan Zhang, Kilian M. Pohl, Li Fei-Fei, Juan Carlos Niebles, Ehsan Adeli

Batch Normalization (BN) and its variants have delivered tremendous success in combating the covariate shift induced by the training step of deep learning methods. [Expand]

Wednesday Poster Session

Yuqing Wang, Zhaoliang Xu, Xinlong Wang, Chunhua Shen, Baoshan Cheng, Hao Shen, Huaxia Xia

Video instance segmentation (VIS) is the task that requires simultaneously classifying, segmenting and tracking object instances of interest in video. [Expand]

Wednesday Poster Session

Nontawat Tritrong, Pitchaporn Rewatbowornwong, Supasorn Suwajanakorn

While GANs have shown success in realistic image generation, the idea of using GANs for other tasks unrelated to synthesis is underexplored. [Expand]

Tuesday Poster Session

Petr Kellnhofer, Lars C. Jebe, Andrew Jones, Ryan Spicer, Kari Pulli, Gordon Wetzstein

Novel view synthesis is a challenging and ill-posed inverse rendering problem. [Expand]

Tuesday Poster Session

Hyunsu Kim, Yunjey Choi, Junho Kim, Sungjoo Yoo, Youngjung Uh

Generative adversarial networks (GANs) synthesize realistic images from random latent vectors. [Expand]

Monday Poster Session

Atul Ingle, Trevor Seets, Mauro Buttafava, Shantanu Gupta, Alberto Tosi, Mohit Gupta, Andreas Velten

Digital camera pixels measure image intensities by converting incident light energy into an analog electrical current, and then digitizing it into a fixed-width binary representation. [Expand]

Wednesday Poster Session

Madhawa Vidanapathirana, Qirui Wu, Yasutaka Furukawa, Angel X. Chang, Manolis Savva

We address the task of converting a floorplan and a set of associated photos of a residence into a textured 3D mesh model, a task which we call Plan2Scene. [Expand]

Wednesday Poster Session

Jennifer J. Sun, Ann Kennedy, Eric Zhan, David J. Anderson, Yisong Yue, Pietro Perona

Specialized domain knowledge is often necessary to accurately annotate training sets for in-depth analysis, but can be burdensome and time-consuming to acquire from domain experts. [Expand]

Tuesday Poster Session

Lei Ke, Yu-Wing Tai, Chi-Keung Tang

Segmenting highly-overlapping objects is challenging, because typically no distinction is made between real object contours and occlusion boundaries. [Expand]

Tuesday Poster Session

Despoina Paschalidou, Angelos Katharopoulos, Andreas Geiger, Sanja Fidler

Impressive progress in 3D shape extraction led to representations that can capture object geometries with high fidelity. [Expand]

Tuesday Poster Session

Alvaro Parra, Shin-Fang Chng, Tat-Jun Chin, Anders Eriksson, Ian Reid

Under mild conditions on the noise level of the measurements, rotation averaging satisfies strong duality, which enables global solutions to be obtained via semidefinite programming (SDP) relaxation. [Expand]

Tuesday Poster Session

Rui Huang, Yixuan Li

Detecting out-of-distribution (OOD) inputs is a central challenge for safely deploying machine learning models in the real world. [Expand]

Wednesday Poster Session

Ji Lin, Richard Zhang, Frieder Ganz, Song Han, Jun-Yan Zhu

Generative adversarial networks (GANs) have enabled photorealistic image synthesis and editing. [Expand]

Thursday Poster Session

Yinghao Xu, Yujun Shen, Jiapeng Zhu, Ceyuan Yang, Bolei Zhou

Generative Adversarial Networks (GANs) have recently advanced image synthesis by learning the underlying distribution of the observed data. [Expand]

Tuesday Poster Session

Miguel Jaques, Michael Burke, Timothy M. Hospedales

Learning low-dimensional latent state space dynamics models has proven powerful for enabling vision-based planning and learning for control. [Expand]

Tuesday Poster Session

Siyuan Qiao, Liang-Chieh Chen, Alan Yuille

Many modern object detectors demonstrate outstanding performances by using the mechanism of looking and thinking twice. [Expand]

Wednesday Poster Session

Zongze Wu, Dani Lischinski, Eli Shechtman

We explore and analyze the latent style space of StyleGAN2, a state-of-the-art architecture for image generation, using models pretrained on several different datasets. [Expand]

Thursday Poster Session

Huiyu Wang, Yukun Zhu, Hartwig Adam, Alan Yuille, Liang-Chieh Chen

We present MaX-DeepLab, the first end-to-end model for panoptic segmentation. [Expand]

Tuesday Poster Session

Guanzhe Hong, Zhiyuan Mao, Xiaojun Lin, Stanley H. Chan

Feature-based student-teacher learning, a training method that encourages the student's hidden features to mimic those of the teacher network, is empirically successful in transferring the knowledge from a pre-trained teacher network to the student network. [Expand]

Thursday Poster Session

Chien-Yao Wang, Alexey Bochkovskiy, Hong-Yuan Mark Liao

We show that the YOLOv4 object detection neural network based on the CSP approach, scales both up and down and is applicable to small and large networks while maintaining optimal speed and accuracy. [Expand]

Thursday Poster Session

Pratul P. Srinivasan, Boyang Deng, Xiuming Zhang, Matthew Tancik, Ben Mildenhall, Jonathan T. Barron

We present a method that takes as input a set of images of a scene illuminated by unconstrained known lighting, and produces as output a 3D representation that can be rendered from novel viewpoints under arbitrary lighting conditions. [Expand]

Wednesday Poster Session

Yuan-Hong Liao, Amlan Kar, Sanja Fidler

Data is the engine of modern computer vision, which necessitates collecting large-scale datasets. [Expand]

Tuesday Poster Session

Tal Daniel, Aviv Tamar

The recently introduced introspective variational autoencoder (IntroVAE) exhibits outstanding image generations, and allows for amortized inference using an image encoder. [Expand]

Tuesday Poster Session

Hung-Yu Tseng, Lu Jiang, Ce Liu, Ming-Hsuan Yang, Weilong Yang

Recent years have witnessed the rapid progress of generative adversarial networks (GANs). [Expand]

Wednesday Poster Session

Dmytro Kotovenko, Matthias Wright, Arthur Heimbrecht, Bjorn Ommer

There have been many successful implementations of neural style transfer in recent years. [Expand]

Thursday Poster Session

Florian Kluger, Hanno Ackermann, Eric Brachmann, Michael Ying Yang, Bodo Rosenhahn

Humans perceive and construct the surrounding world as an arrangement of simple parametric models. [Expand]

Thursday Poster Session

Sanghyun Son, Kyoung Mu Lee

Deep CNNs have achieved significant successes in image processing and its applications, including single image super-resolution (SR). [Expand]

Wednesday Poster Session

Wei Yin, Jianming Zhang, Oliver Wang, Simon Niklaus, Long Mai, Simon Chen, Chunhua Shen

Despite significant progress in monocular depth estimation in the wild, recent state-of-the-art methods cannot be used to recover accurate 3D scene shape due to an unknown depth shift induced by shift-invariant reconstruction losses used in mixed-data depth prediction training, and possible unknown camera focal length. [Expand]

Monday Poster Session

Lea Muller, Ahmed A. A. Osman, Siyu Tang, Chun-Hao P. Huang, Michael J. Black

People touch their face 23 times an hour, they cross their arms and legs, put their hands on their hips, etc. [Expand]

Wednesday Poster Session

Han Zhang, Jing Yu Koh, Jason Baldridge, Honglak Lee, Yinfei Yang

The output of text-to-image synthesis systems should be coherent, clear, photo-realistic scenes with high semantic fidelity to their conditioned text descriptions. [Expand]

Monday Poster Session

Mahmoud Afifi, Marcus A. Brubaker, Michael S. Brown

While generative adversarial networks (GANs) can successfully produce high-quality images, they can be challenging to control. [Expand]

Wednesday Poster Session

Michael Ramamonjisoa, Michael Firman, Jamie Watson, Vincent Lepetit, Daniyar Turmukhambetov

We present a novel method for predicting accurate depths from monocular images with high efficiency. [Expand]

Wednesday Poster Session

Yufu Wang, Nikos Kolotouros, Kostas Daniilidis, Marc Badger

Animals are diverse in shape, but building a deformable shape model for a new species is not always possible due to the lack of 3D data. [Expand]

Thursday Poster Session

Bin Yan, Houwen Peng, Kan Wu, Dong Wang, Jianlong Fu, Huchuan Lu

Object tracking has achieved significant progress over the past few years. [Expand]

Thursday Poster Session

Evonne Ng, Shiry Ginosar, Trevor Darrell, Hanbyul Joo

We propose a novel learned deep prior of body motion for 3D hand shape synthesis and estimation in the domain of conversational gestures. [Expand]

Thursday Poster Session

Baris Gecer, Jiankang Deng, Stefanos Zafeiriou

The last few years have witnessed the great success of non-linear generative models in synthesizing high-quality photorealistic face images. [Expand]

Wednesday Poster Session

K J Joseph, Salman Khan, Fahad Shahbaz Khan, Vineeth N Balasubramanian

Humans have a natural instinct to identify unknown object instances in their environments. [Expand]

Tuesday Poster Session

Dan Kondratyuk, Liangzhe Yuan, Yandong Li, Li Zhang, Mingxing Tan, Matthew Brown, Boqing Gong

We present Mobile Video Networks (MoViNets), a family of computation and memory efficient video networks that can operate on streaming video for online inference. [Expand]

Friday Poster Session

Christoph Lassner, Michael Zollhofer

We propose Pulsar, an efficient sphere-based differentiable rendering module that is orders of magnitude faster than competing techniques, modular, and easy-to-use due to its tight integration with PyTorch. [Expand]

Monday Poster Session

Ning Wang, Wengang Zhou, Jie Wang, Houqiang Li

In video object tracking, there exist rich temporal contexts among successive frames, which have been largely overlooked in existing trackers. [Expand]

Monday Poster Session

Kehong Gong, Jianfeng Zhang, Jiashi Feng

Existing 3D human pose estimators suffer poor generalization performance to new datasets, largely due to the limited diversity of 2D-3D pose pairs in the training data. [Expand]

Wednesday Poster Session

Donghwan Lee, Soohyun Ryu, Suyong Yeon, Yonghan Lee, Deokhwa Kim, Cheolho Han, Yohann Cabon, Philippe Weinzaepfel, Nicolas Guerin, Gabriela Csurka, Martin Humenberger

Estimating the precise location of a camera using visual localization enables interesting applications such as augmented reality or robot navigation. [Expand]

Tuesday Poster Session

Tao Yu, Zerong Zheng, Kaiwen Guo, Pengpeng Liu, Qionghai Dai, Yebin Liu

Human volumetric capture is a long-standing topic in computer vision and computer graphics. [Expand]

Tuesday Poster Session

Hongxu Yin, Arun Mallya, Arash Vahdat, Jose M. Alvarez, Jan Kautz, Pavlo Molchanov

Training deep neural networks requires gradient estimation from data batches to update parameters. [Expand]

Friday Poster Session

Philipp Henzler, Jeremy Reizenstein, Patrick Labatut, Roman Shapovalov, Tobias Ritschel, Andrea Vedaldi, David Novotny

Recently, numerous works have attempted to learn 3D reconstructors of textured 3D models of visual categories given a training set of annotated static images of objects. [Expand]

Tuesday Poster Session

Qi Fang, Qing Shuai, Junting Dong, Hujun Bao, Xiaowei Zhou

In this paper, we introduce the new task of reconstructing 3D human pose from a single image in which we can see the person and the person's image through a mirror. [Expand]

Thursday Poster Session

Avisek Lahiri, Vivek Kwatra, Christian Frueh, John Lewis, Chris Bregler

In this paper, we present a video-based learning framework for animating personalized 3D talking faces from audio. [Expand]

Monday Poster Session

Julieta Martinez, Jashan Shewakramani, Ting Wei Liu, Ioan Andrei Barsan, Wenyuan Zeng, Raquel Urtasun

Compressing large neural networks is an important step for their deployment in resource-constrained computational platforms. [Expand]

Friday Poster Session

Takuhiro Kaneko, Tatsuya Harada

Generative adversarial networks (GANs) have gained considerable attention owing to their ability to reproduce images. [Expand]

Thursday Poster Session

Hang Zhou, Yasheng Sun, Wayne Wu, Chen Change Loy, Xiaogang Wang, Ziwei Liu

While accurate lip synchronization has been achieved for arbitrary-subject audio-driven talking face generation, the problem of how to efficiently drive the head pose remains. [Expand]

Tuesday Poster Session

Yichao Zhou, Shichen Liu, Yi Ma

Recent advances have shown that symmetry, a structural prior that most objects exhibit, can support a variety of single-view 3D understanding tasks. [Expand]

Friday Poster Session

Adel Ahmadyan, Liangkai Zhang, Artsiom Ablavatski, Jianing Wei, Matthias Grundmann

3D object detection has recently become popular due to many applications in robotics, augmented reality, autonomy, and image retrieval. [Expand]

Wednesday Poster Session

Christoph Feichtenhofer, Haoqi Fan, Bo Xiong, Ross Girshick, Kaiming He

We present a large-scale study on unsupervised spatiotemporal representation learning from videos. [Expand]

Tuesday Poster Session

Honglie Chen, Weidi Xie, Triantafyllos Afouras, Arsha Nagrani, Andrea Vedaldi, Andrew Zisserman

The objective of this work is to localize sound sources that are visible in a video without using manual annotations. [Expand]

Friday Poster Session

Filippo Aleotti, Matteo Poggi, Stefano Mattoccia

This paper deals with the scarcity of data for training optical flow networks, highlighting the limitations of existing sources such as labeled synthetic datasets or unlabeled real videos. [Expand]

Thursday Poster Session

Lukas Hoyer, Dengxin Dai, Yuhua Chen, Adrian Koring, Suman Saha, Luc Van Gool

Training deep networks for semantic segmentation requires large amounts of labeled training data, which presents a major challenge in practice, as labeling segmentation masks is a highly labor-intensive process. [Expand]

Wednesday Poster Session

Ruohan Gao, Kristen Grauman

We introduce a new approach for audio-visual speech separation. [Expand]

Thursday Poster Session

Yulei Niu, Kaihua Tang, Hanwang Zhang, Zhiwu Lu, Xian-Sheng Hua, Ji-Rong Wen

Recent VQA models may tend to rely on language bias as a shortcut and thus fail to sufficiently learn the multi-modal knowledge from both vision and language. [Expand]

Thursday Poster Session

Matthew J. Vowels, Necati Cihan Camgoz, Richard Bowden

Disentangled representations support a range of downstream tasks including causal reasoning, generative modeling, and fair machine learning. [Expand]

Wednesday Poster Session

Zhi Tian, Chunhua Shen, Xinlong Wang, Hao Chen

We present a high-performance method that can achieve mask-level instance segmentation with only bounding-box annotations for training. [Expand]

Tuesday Poster Session

Michael Wray, Hazel Doughty, Dima Damen

Current video retrieval efforts all found their evaluation on an instance-based assumption, that only a single caption is relevant to a query video and vice versa. [Expand]

Tuesday Poster Session

Eric R. Chan, Marco Monteiro, Petr Kellnhofer, Jiajun Wu, Gordon Wetzstein

We have witnessed rapid progress on 3D-aware image synthesis, leveraging recent advances in generative visual models and neural rendering. [Expand]

Tuesday Poster Session

Piotr Dollar, Mannat Singh, Ross Girshick

In this work we analyze strategies for convolutional neural network scaling; that is, the process of scaling a base convolutional network to endow it with greater computational complexity and consequently representational power. [Expand]

Monday Poster Session

Denys Rozumnyi, Martin R. Oswald, Vittorio Ferrari, Jiri Matas, Marc Pollefeys

Objects moving at high speed appear significantly blurred when captured with cameras. [Expand]

Tuesday Poster Session

Pengwan Yang, Pascal Mettes, Cees G. M. Snoek

This paper introduces the task of few-shot common action localization in time and space. [Expand]

Friday Poster Session

Sahil Singla, Besmira Nushi, Shital Shah, Ece Kamar, Eric Horvitz

Traditional evaluation metrics for learned models that report aggregate scores over a test set are insufficient for surfacing important and informative patterns of failure over features and instances. [Expand]

Thursday Poster Session

Vitor Albiero, Xingyu Chen, Xi Yin, Guan Pang, Tal Hassner

We propose real-time, six degrees of freedom (6DoF), 3D face pose estimation without face detection or landmark localization. [Expand]

Wednesday Poster Session

Jie Liang, Hui Zeng, Lei Zhang

Existing image-to-image translation (I2IT) methods are either constrained to low-resolution images or long inference time due to their heavy computational burden on the convolution of high-resolution feature maps. [Expand]

Wednesday Poster Session

Lvmin Zhang, Chengze Li, Edgar Simo-Serra, Yi Ji, Tien-Tsin Wong, Chunping Liu

Flat filling is a critical step in digital artistic content creation with the objective of filling line arts with flat colors. [Expand]

Wednesday Poster Session

Julian Ost, Fahim Mannan, Nils Thuerey, Julian Knodt, Felix Heide

Recent implicit neural rendering methods have demonstrated that it is possible to learn accurate view synthesis for complex scenes by predicting their volumetric density and color supervised solely by a set of RGB images. [Expand]

Tuesday Poster Session

Gengshan Yang, Deqing Sun, Varun Jampani, Daniel Vlasic, Forrester Cole, Huiwen Chang, Deva Ramanan, William T. Freeman, Ce Liu

Remarkable progress has been made in 3D reconstruction of rigid structures from a video or a collection of images. [Expand]

Friday Poster Session

Isma Hadji, Konstantinos G. Derpanis, Allan D. Jepson

We introduce a weakly supervised method for representation learning based on aligning temporal sequences (e.g., videos) of the same process (e.g., human action). [Expand]

Wednesday Poster Session

Eric Heitz, Kenneth Vanhoey, Thomas Chambon, Laurent Belcour

We address the problem of computing a textural loss based on the statistics extracted from the feature activations of a convolutional neural network optimized for object recognition (e.g. [Expand]

Wednesday Poster Session

Kelvin C.K. Chan, Xintao Wang, Xiangyu Xu, Jinwei Gu, Chen Change Loy

We show that pre-trained Generative Adversarial Networks (GANs), e.g., StyleGAN, can be used as a latent bank to improve the restoration quality of large-factor image super-resolution (SR). [Expand]

Thursday Poster Session

Tomas Jakab, Richard Tucker, Ameesh Makadia, Jiajun Wu, Noah Snavely, Angjoo Kanazawa

We introduce KeypointDeformer, a novel unsupervised method for shape control through automatically discovered 3D keypoints. [Expand]

Thursday Poster Session

Lele Chen, Chen Cao, Fernando De la Torre, Jason Saragih, Chenliang Xu, Yaser Sheikh

3D video avatars can empower virtual communications by providing compression, privacy, entertainment, and a sense of presence in AR/VR. [Expand]

Thursday Poster Session

Stephen Hausler, Sourav Garg, Ming Xu, Michael Milford, Tobias Fischer

Visual Place Recognition is a challenging task for robotics and autonomous systems, which must deal with the twin problems of appearance and viewpoint change in an always changing world. [Expand]

Thursday Poster Session

Ji Hou, Benjamin Graham, Matthias Niessner, Saining Xie

The rapid progress in 3D scene understanding has come with growing demand for data; however, collecting and annotating 3D scenes (e.g. [Expand]

Friday Poster Session

Zhicheng Huang, Zhaoyang Zeng, Yupan Huang, Bei Liu, Dongmei Fu, Jianlong Fu

We study on joint learning of Convolutional Neural Network (CNN) and Transformer for vision-language pre-training (VLPT) which aims to learn cross-modal alignments from millions of image-text pairs. [Expand]

Thursday Poster Session

Ramprasaath R. Selvaraju, Karan Desai, Justin Johnson, Nikhil Naik

Recent advances in self-supervised learning (SSL) have largely closed the gap with supervised ImageNet pretraining. [Expand]

Wednesday Poster Session

Yunyang Xiong, Hanxiao Liu, Suyog Gupta, Berkin Akin, Gabriel Bender, Yongzhe Wang, Pieter-Jan Kindermans, Mingxing Tan, Vikas Singh, Bo Chen

Inverted bottleneck layers, which are built upon depthwise convolutions, have been the predominant building blocks in state-of-the-art object detection models on mobile devices. [Expand]

Tuesday Poster Session

Dongyoon Han, Sangdoo Yun, Byeongho Heo, YoungJoon Yoo

Designing an efficient model within the limited computational cost is challenging. [Expand]

Monday Poster Session

Kiana Ehsani, Winson Han, Alvaro Herrasti, Eli VanderBilt, Luca Weihs, Eric Kolve, Aniruddha Kembhavi, Roozbeh Mottaghi

The domain of Embodied AI has recently witnessed substantial progress, particularly in navigating agents within their environments. [Expand]

Tuesday Poster Session

Daniel Barath, Dmytro Mishkin, Ivan Eichhardt, Ilia Shipachev, Jiri Matas

We propose ways to speed up the initial pose-graph generation for global Structure-from-Motion algorithms. [Expand]

Thursday Poster Session

Mohamad Shahbazi, Zhiwu Huang, Danda Pani Paudel, Ajad Chhatkuli, Luc Van Gool

Generative adversarial networks (GANs) have shown impressive results in both unconditional and conditional image generation. [Expand]

Thursday Poster Session

Jianfeng Wang, Lin Song, Zeming Li, Hongbin Sun, Jian Sun, Nanning Zheng

Mainstream object detectors based on the fully convolutional network has achieved impressive performance. [Expand]

Friday Poster Session

Olivia Wiles, Sebastien Ehrhardt, Andrew Zisserman

We propose a new approach to determine correspondences between image pairs in the wild under large changes in illumination, viewpoint, context, and material. [Expand]

Friday Poster Session

Zhenyu Chen, Ali Gholami, Matthias Niessner, Angel X. Chang

We introduce the new task of dense captioning in RGB-D scans. [Expand]

Tuesday Poster Session

Filippos Kokkinos, Iasonas Kokkinos

Monocular 3D reconstruction of articulated object categories is challenging due to the lack of training data and the inherent ill-posedness of the problem. [Expand]

Monday Poster Session

Nikita Araslanov, Stefan Roth

We propose an approach to domain adaptation for semantic segmentation that is both practical and highly accurate. [Expand]

Thursday Poster Session

Matthew Tancik, Ben Mildenhall, Terrance Wang, Divi Schmidt, Pratul P. Srinivasan, Jonathan T. Barron, Ren Ng

Coordinate-based neural representations have shown significant promise as an alternative to discrete, array-based representations for complex low dimensional signals. [Expand]

Tuesday Poster Session

Pedro Morgado, Nuno Vasconcelos, Ishan Misra

We present a self-supervised learning approach to learn audio-visual representations from video and audio. [Expand]

Thursday Poster Session

Yan Zhang, Michael J. Black, Siyu Tang

A key step towards understanding human behavior is the prediction of 3D human motion. [Expand]

Tuesday Poster Session

Pei Wang, Yijun Li, Nuno Vasconcelos

Extensive research in neural style transfer methods has shown that the correlation between features extracted by a pre-trained VGG network has remarkable ability to capture the visual style of an image. [Expand]

Monday Poster Session

Yun Chen, Frieda Rong, Shivam Duggal, Shenlong Wang, Xinchen Yan, Sivabalan Manivasagam, Shangjie Xue, Ersin Yumer, Raquel Urtasun

Scalable sensor simulation is an important yet challenging open problem for safety-critical domains such as self-driving. [Expand]

Wednesday Poster Session

Xiangning Chen, Cihang Xie, Mingxing Tan, Li Zhang, Cho-Jui Hsieh, Boqing Gong

Data augmentation has become a de facto component for training high-performance deep image classifiers, but its potential is under-explored for object detection. [Expand]

Friday Poster Session

Junhwa Hur, Stefan Roth

Estimating 3D scene flow from a sequence of monocular images has been gaining increased attention due to the simple, economical capture setup. [Expand]

Monday Poster Session

Jie Liang, Hui Zeng, Miaomiao Cui, Xuansong Xie, Lei Zhang

Different from general photo retouching tasks, portrait photo retouching (PPR), which aims to enhance the visual quality of a collection of flat-looking portrait photos, has its special and practical requirements such as human-region priority (HRP) and group-level consistency (GLC). [Expand]

Monday Poster Session

Xu Yang, Hanwang Zhang, Guojun Qi, Jianfei Cai

We present a novel attention mechanism: Causal Attention (CATT), to remove the ever-elusive confounding effect in existing attention-based vision-language models. [Expand]

Wednesday Poster Session

Pengchuan Zhang, Xiujun Li, Xiaowei Hu, Jianwei Yang, Lei Zhang, Lijuan Wang, Yejin Choi, Jianfeng Gao

This paper presents a detailed study of improving vision features and develops an improved object detection model for vision language (VL) tasks. [Expand]

Tuesday Poster Session

Freyr Sverrisson, Jean Feydy, Bruno E. Correia, Michael M. Bronstein

Proteins' biological functions are defined by the geometric and chemical structure of their 3D molecular surfaces. [Expand]

Thursday Poster Session

Xin Yuan, Zhe Lin, Jason Kuen, Jianming Zhang, Yilin Wang, Michael Maire, Ajinkya Kale, Baldo Faieta

We develop an approach to learning visual representations that embraces multimodal data, driven by a combination of intra- and inter-modal similarity preservation objectives. [Expand]

Tuesday Poster Session

Daniel Rebain, Wei Jiang, Soroosh Yazdani, Ke Li, Kwang Moo Yi, Andrea Tagliasacchi

With the advent of Neural Radiance Fields (NeRF), neural networks can now render novel views of a 3D scene with quality that fools the human eye. [Expand]

Thursday Poster Session

Jiashun Wang, Huazhe Xu, Jingwei Xu, Sifei Liu, Xiaolong Wang

Synthesizing 3D human motion plays an important role in many graphics applications as well as understanding human activity. [Expand]

Wednesday Poster Session

Amanpreet Singh, Guan Pang, Mandy Toh, Jing Huang, Wojciech Galuba, Tal Hassner

A crucial component for the scene text based reasoning required for TextVQA and TextCaps datasets involve detecting and recognizing text present in the images using an optical character recognition (OCR) system. [Expand]

Wednesday Poster Session

Ahmed Taha, Abhinav Shrivastava, Larry S. Davis

Deep learning relies on the availability of a large corpus of data (labeled or unlabeled). [Expand]

Thursday Poster Session

Seong Hun Lee, Javier Civera

We propose a novel method for estimating the global rotations of the cameras independently of their positions and the scene structure. [Expand]

Monday Poster Session

Viresh Ranjan, Udbhav Sharma, Thu Nguyen, Minh Hoai

Existing works on visual counting primarily focus on one specific category at a time, such as people, animals, and cells. [Expand]

Tuesday Poster Session

Minshan Xie, Menghan Xia, Tien-Tsin Wong

As a popular entertainment art form, manga enriches the line drawings details with bitonal screentones. [Expand]

Thursday Poster Session

Tolga Birdal, Vladislav Golyanik, Christian Theobalt, Leonidas J. Guibas

We present QuantumSync, the first quantum algorithm for solving a synchronization problem in the context of computer vision. [Expand]

Thursday Poster Session

Peize Sun, Rufeng Zhang, Yi Jiang, Tao Kong, Chenfeng Xu, Wei Zhan, Masayoshi Tomizuka, Lei Li, Zehuan Yuan, Changhu Wang, Ping Luo

We present Sparse R-CNN, a purely sparse method for object detection in images. [Expand]

Thursday Poster Session

Toby Perrett, Alessandro Masullo, Tilo Burghardt, Majid Mirmehdi, Dima Damen

We propose a novel approach to few-shot action recognition, finding temporally-corresponding frame tuples between the query and videos in the support set. [Expand]

Monday Poster Session

Zhaoyang Huang, Han Zhou, Yijin Li, Bangbang Yang, Yan Xu, Xiaowei Zhou, Hujun Bao, Guofeng Zhang, Hongsheng Li

Visual localization is of great importance in robotics and computer vision. [Expand]

Tuesday Poster Session

Wentao Yuan, Zhaoyang Lv, Tanner Schmidt, Steven Lovegrove

We present STaR, a novel method that performs Self-supervised Tracking and Reconstruction of dynamic scenes with rigid motion from multi-view RGB videos without any manual annotation. [Expand]

Thursday Poster Session

Mahmoud Afifi, Konstantinos G. Derpanis, Bjorn Ommer, Michael S. Brown

Capturing photographs with wrong exposures remains a major source of errors in camera-based imaging. [Expand]

Wednesday Poster Session

Zan Gojcic, Or Litany, Andreas Wieser, Leonidas J. Guibas, Tolga Birdal

We propose a data-driven scene flow estimation algorithm exploiting the observation that many 3D scenes can be explained by a collection of agents moving as rigid bodies. [Expand]

Tuesday Poster Session

Shreyas Hampali, Sinisa Stekovic, Sayan Deb Sarkar, Chetan S. Kumar, Friedrich Fraundorfer, Vincent Lepetit

We explore how a general AI algorithm can be used for 3D scene understanding to reduce the need for training data. [Expand]

Thursday Poster Session

Yuming Jiang, Kelvin C.K. Chan, Xintao Wang, Chen Change Loy, Ziwei Liu

Reference-based Super-Resolution (Ref-SR) has recently emerged as a promising paradigm to enhance a low-resolution (LR) input image by introducing an additional high-resolution (HR) reference image. [Expand]

Monday Poster Session

Haochen Wang, Xiaolong Jiang, Haibing Ren, Yao Hu, Song Bai

In this work we present SwiftNet for real-time semi-supervised video object segmentation (one-shot VOS), which reports 77.8% J&F and 70 FPS on DAVIS 2017 validation dataset, leading all present solutions in overall accuracy and speed performance. [Expand]

Monday Poster Session

Minjun Li, Yanghua Jin, Huachun Zhu

Generative adversarial networks (GANs) can generate high-quality images from sampled latent codes. [Expand]

Tuesday Poster Session

Goutam Bhat, Martin Danelljan, Luc Van Gool, Radu Timofte

While single-image super-resolution (SISR) has attracted substantial interest in recent years, the proposed approaches are limited to learning image priors in order to add high frequency details. [Expand]

Wednesday Poster Session

Tianyu Ding, Luming Liang, Zhihui Zhu, Ilya Zharkov

DNN-based frame interpolation--that generates the intermediate frames given two consecutive frames--typically relies on heavy model architectures with a huge number of features, preventing them from being deployed on systems with limited resources, e.g., mobile devices. [Expand]

Wednesday Poster Session

Menglin Jia, Zuxuan Wu, Austin Reiter, Claire Cardie, Serge Belongie, Ser-Nam Lim

An image is worth a thousand words, conveying information that goes beyond the physical visual content therein. [Expand]

Thursday Poster Session

Amit Raj, Michael Zollhofer, Tomas Simon, Jason Saragih, Shunsuke Saito, James Hays, Stephen Lombardi

Acquisition and rendering of photo-realistic human heads is a highly challenging research problem of particular importance for virtual telepresence. [Expand]

Thursday Poster Session

Bohan Wu, Suraj Nair, Roberto Martin-Martin, Li Fei-Fei, Chelsea Finn

A video prediction model that generalizes to diverse scenes would enable intelligent agents such as robots to perform a variety of tasks via planning with the model. [Expand]

Monday Poster Session

Jean-Baptiste Cordonnier, Aravindh Mahendran, Alexey Dosovitskiy, Dirk Weissenborn, Jakob Uszkoreit, Thomas Unterthiner

Neural Networks require large amounts of memory and compute to process high resolution images, even when only a small part of the image is actually informative for the task at hand. [Expand]

Monday Poster Session

Arthur Douillard, Yifu Chen, Arnaud Dapogny, Matthieu Cord

Deep learning approaches are nowadays ubiquitously used to tackle computer vision tasks such as semantic segmentation, requiring large datasets and substantial computational power. [Expand]

Tuesday Poster Session

Ningning Ma, Xiangyu Zhang, Ming Liu, Jian Sun

We present a simple, effective, and general activation function we term ACON which learns to activate the neurons or not. [Expand]

Wednesday Poster Session

Kento Nishi, Yi Ding, Alex Rich, Tobias Hollerer

Imperfect labels are ubiquitous in real-world datasets. [Expand]

Wednesday Poster Session

Nishant Rai, Haofeng Chen, Jingwei Ji, Rishi Desai, Kazuki Kozuka, Shun Ishizaka, Ehsan Adeli, Juan Carlos Niebles

Existing research on action recognition treats activities as monolithic events occurring in videos. [Expand]

Wednesday Poster Session

Fanbo Xiang, Zexiang Xu, Milos Hasan, Yannick Hold-Geoffroy, Kalyan Sunkavalli, Hao Su

Recent work has demonstrated that volumetric scene representations combined with differentiable volume rendering can enable photo-realistic rendering for challenging scenes that mesh reconstruction fails on. [Expand]

Wednesday Poster Session

Hui Wu, Yupeng Gao, Xiaoxiao Guo, Ziad Al-Halah, Steven Rennie, Kristen Grauman, Rogerio Feris

Conversational interfaces for the detail-oriented retail fashion domain are more natural, expressive, and user friendly than classical keyword-based search interfaces. [Expand]

Wednesday Poster Session

Qianli Ma, Shunsuke Saito, Jinlong Yang, Siyu Tang, Michael J. Black

Learning to model and reconstruct humans in clothing is challenging due to articulation, non-rigid deformation, and varying clothing types and topologies. [Expand]

Friday Poster Session

Baptiste Angles, Yuhe Jin, Simon Kornblith, Andrea Tagliasacchi, Kwang Moo Yi

We propose a deep network that can be trained to tackle image reconstruction and classification problems that involve detection of multiple object instances, without any supervision regarding their whereabouts. [Expand]

Monday Poster Session

Ziyan Wang, Timur Bagautdinov, Stephen Lombardi, Tomas Simon, Jason Saragih, Jessica Hodgins, Michael Zollhofer

Photorealistic rendering of dynamic humans is an important ability for telepresence systems, virtual shopping, synthetic data generation, and more. [Expand]

Tuesday Poster Session

Shugao Ma, Tomas Simon, Jason Saragih, Dawei Wang, Yuecheng Li, Fernando De la Torre, Yaser Sheikh

Telecommunication with photorealistic avatars in virtual or augmented reality is a promising path for achieving authentic face-to-face communication in 3D over remote physical distances. [Expand]

Monday Poster Session

Arka Sadhu, Tanmay Gupta, Mark Yatskar, Ram Nevatia, Aniruddha Kembhavi

We propose a new framework for understanding and representing related salient events in a video using visual semantic role labeling. [Expand]

Tuesday Poster Session

Grant Van Horn, Elijah Cole, Sara Beery, Kimberly Wilber, Serge Belongie, Oisin Mac Aodha

Recent progress in self-supervised learning has resulted in models that are capable of extracting rich representations from image collections without requiring any explicit label supervision. [Expand]

Thursday Poster Session

Ivan Skorokhodov, Savva Ignatyev, Mohamed Elhoseiny

In most existing learning systems, images are typically viewed as 2D pixel arrays. [Expand]

Wednesday Poster Session

Chuanxia Zheng, Tat-Jen Cham, Jianfei Cai

We propose a novel spatially-correlative loss that is simple, efficient, and yet effective for preserving scene structure consistency while supporting large appearance changes during unpaired image-to-image (I2I) translation. [Expand]

Friday Poster Session

Lucy Chai, Jun-Yan Zhu, Eli Shechtman, Phillip Isola, Richard Zhang

Recent generative models can synthesize "views" of artificial images that mimic real-world variations, such as changes in color or pose, simply by learning from unlabeled image collections. [Expand]

Thursday Poster Session

Seunghwan Choi, Sunghyun Park, Minsoo Lee, Jaegul Choo

The task of image-based virtual try-on aims to transfer a target clothing item onto the corresponding region of a person, which is commonly tackled by fitting the item to the desired body part and fusing the warped item with the person. [Expand]

Thursday Poster Session

Angela Dai, Yawar Siddiqui, Justus Thies, Julien Valentin, Matthias Niessner

We present SPSG, a novel approach to generate high-quality, colored 3D models of scenes from RGB-D scan observations by learning to infer unobserved scene geometry and color in a self-supervised fashion. [Expand]

Monday Poster Session

Corentin Kervadec, Theo Jaunet, Grigory Antipov, Moez Baccouche, Romain Vuillemot, Christian Wolf

Since its inception, Visual Question Answering (VQA) is notoriously known as a task, where models are prone to exploit biases in datasets to find shortcuts instead of performing high-level reasoning. [Expand]

Tuesday Poster Session

Remi Pautrat, Juan-Ting Lin, Viktor Larsson, Martin R. Oswald, Marc Pollefeys

Compared to feature point detection and description, detecting and matching line segments offer additional challenges. [Expand]

Thursday Poster Session

Diego Martin Arroyo, Janis Postels, Federico Tombari

Generative models able to synthesize layouts of different kinds (e.g. [Expand]

Thursday Poster Session

Kevin Lin, Lijuan Wang, Zicheng Liu

We present a new method, called MEsh TRansfOrmer (METRO), to reconstruct 3D human pose and mesh vertices from a single image. [Expand]

Monday Poster Session

Shunsuke Saito, Jinlong Yang, Qianli Ma, Michael J. Black

We present SCANimate, an end-to-end trainable framework that takes raw 3D scans of a clothed human and turns them into an animatable avatar. [Expand]

Tuesday Poster Session

Yuying Ge, Yibing Song, Ruimao Zhang, Chongjian Ge, Wei Liu, Ping Luo

Image virtual try-on aims to fit a garment image (target clothes) to a person image. [Expand]

Wednesday Poster Session

Shengyu Huang, Zan Gojcic, Mikhail Usvyatsov, Andreas Wieser, Konrad Schindler

We introduce PREDATOR, a model for pairwise pointcloud registration with deep attention to the overlap region. [Expand]

Tuesday Poster Session

Utkarsh Ojha, Yijun Li, Jingwan Lu, Alexei A. Efros, Yong Jae Lee, Eli Shechtman, Richard Zhang

Training generative models, such as GANs, on a target domain containing limited examples (e.g., 10) can easily result in overfitting. [Expand]

Wednesday Poster Session

Stephan Alaniz, Diego Marcos, Bernt Schiele, Zeynep Akata

Integrated interpretability without sacrificing the prediction accuracy of decision making algorithms has the potential of greatly improving their value to the user. [Expand]

Thursday Poster Session

Qing Jin, Jian Ren, Oliver J. Woodford, Jiazhuo Wang, Geng Yuan, Yanzhi Wang, Sergey Tulyakov

Generative Adversarial Networks (GANs) have achieved huge success in generating high-fidelity images, however, they suffer from low efficiency due to tremendous computational cost and bulky memory usage. [Expand]

Thursday Poster Session

Xinyang Li, Shengchuan Zhang, Jie Hu, Liujuan Cao, Xiaopeng Hong, Xudong Mao, Feiyue Huang, Yongjian Wu, Rongrong Ji

Recently, image-to-image translation has made significant progress in achieving both multi-label (i.e., translation conditioned on different labels) and multi-style (i.e., generation with diverse styles) tasks. [Expand]

Wednesday Poster Session

Mohsen Fayyaz, Emad Bahrami, Ali Diba, Mehdi Noroozi, Ehsan Adeli, Luc Van Gool, Jurgen Gall

While state-of-the-art 3D Convolutional Neural Networks (CNN) achieve very good results on action recognition datasets, they are computationally very expensive and require many GFLOPs. [Expand]

Tuesday Poster Session

Shangzhe Wu, Ameesh Makadia, Jiajun Wu, Noah Snavely, Richard Tucker, Angjoo Kanazawa

Recent works have shown exciting results in unsupervised image de-rendering--learning to decompose 3D shape, appearance, and lighting from single-image collections without explicit supervision. [Expand]

Tuesday Poster Session

Haoyang Zhang, Ying Wang, Feras Dayoub, Niko Sunderhauf

Accurately ranking the vast number of candidate detections is crucial for dense object detectors to achieve high performance. [Expand]

Wednesday Poster Session

Diego Ortego, Eric Arazo, Paul Albert, Noel E. O'Connor, Kevin McGuinness

Deep neural networks trained with standard cross-entropy loss memorize noisy labels, which degrades their performance. [Expand]

Tuesday Poster Session

Tianwei Yin, Xingyi Zhou, Philipp Krahenbuhl

Three-dimensional objects are commonly represented as 3D boxes in a point-cloud. [Expand]

Thursday Poster Session

Richard T. Marriott, Sami Romdhani, Liming Chen

Facial recognition using deep convolutional neural networks relies on the availability of large datasets of face images. [Expand]

Thursday Poster Session

Giancarlo Di Biase, Hermann Blum, Roland Siegwart, Cesar Cadena

The inability of state-of-the-art semantic segmentation methods to detect anomaly instances hinders them from being deployed in safety-critical and complex applications, such as autonomous driving. [Expand]

Friday Poster Session

Yue Gao, Fangyun Wei, Jianmin Bao, Shuyang Gu, Dong Chen, Fang Wen, Zhouhui Lian

Cycle consistency is widely used for face editing. [Expand]

Friday Poster Session

Artur Grigorev, Karim Iskakov, Anastasia Ianina, Renat Bashirov, Ilya Zakharkin, Alexander Vakhitov, Victor Lempitsky

We propose a new type of full-body human avatars, which combines parametric mesh-based body model with a neural texture. [Expand]

Tuesday Poster Session

Kunal Chelani, Fredrik Kahl, Torsten Sattler

Visual localization is the problem of estimating the camera pose of a given image with respect to a known scene. [Expand]

Friday Poster Session

Jiaxin Cheng, Ayush Jaiswal, Yue Wu, Pradeep Natarajan, Prem Natarajan

Neural Style Transfer (NST) has quickly evolved from single-style to infinite-style models, also known as Arbitrary Style Transfer (AST). [Expand]

Monday Poster Session

Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, Ling Shao

Image restoration tasks demand a complex balance between spatial details and high-level contextualized information while recovering images. [Expand]

Thursday Poster Session

Colorado J Reed, Sean Metzger, Aravind Srinivas, Trevor Darrell, Kurt Keutzer

A common practice in unsupervised representation learning is to use labeled data to evaluate the quality of the learned representations. [Expand]

Monday Poster Session

Lei Zhu, Qi She, Bin Zhang, Yanye Lu, Zhilin Lu, Duo Li, Jie Hu

Superpixel is generated by automatically clustering pixels in an image into hundreds of compact partitions, which is widely used to perceive the object contours for its excellent contour adherence. [Expand]

Monday Poster Session

Jihwan Bang, Heesu Kim, YoungJoon Yoo, Jung-Woo Ha, Jonghyun Choi

Continual learning is a realistic learning scenario for AI models. [Expand]

Wednesday Poster Session

Massimiliano Mancini, Muhammad Ferjad Naeem, Yongqin Xian, Zeynep Akata

Compositional Zero-Shot learning (CZSL) requires to recognize state-object compositions unseen during training. [Expand]

Tuesday Poster Session

Ruoxi Shi, Zhengrong Xue, Yang You, Cewu Lu

Detecting aligned 3D keypoints is essential under many scenarios such as object tracking, shape retrieval and robotics. [Expand]

Monday Poster Session

Zihang Meng, Licheng Yu, Ning Zhang, Tamara L. Berg, Babak Damavandi, Vikas Singh, Amy Bearman

We introduce a unified framework to jointly model images, text, and human attention traces. [Expand]

Thursday Poster Session

Chaofeng Chen, Xiaoming Li, Lingbo Yang, Xianhui Lin, Lei Zhang, Kwan-Yee K. Wong

Face restoration is important in face image processing, and has been widely studied in recent years. [Expand]

Thursday Poster Session

Ruihui Li, Xianzhi Li, Pheng-Ann Heng, Chi-Wing Fu

Point clouds produced by 3D scanning are often sparse, non-uniform, and noisy. [Expand]

Monday Poster Session

Malik Boudiaf, Hoel Kervadec, Ziko Imtiaz Masud, Pablo Piantanida, Ismail Ben Ayed, Jose Dolz

We show that the way inference is performed in few-shot segmentation tasks has a substantial effect on performances--an aspect often overlooked in the literature in favor of the meta-learning paradigm. [Expand]

Thursday Poster Session

Jiahui Huang, He Wang, Tolga Birdal, Minhyuk Sung, Federica Arrigoni, Shi-Min Hu, Leonidas J. Guibas

We present MultiBodySync, a novel, end-to-end trainable multi-body motion segmentation and rigid registration framework for multiple input 3D point clouds. [Expand]

Wednesday Poster Session

Qingyong Hu, Bo Yang, Sheikh Khalid, Wen Xiao, Niki Trigoni, Andrew Markham

An essential prerequisite for unleashing the potential of supervised deep learning algorithms in the area of 3D scene understanding is the availability of large-scale and richly annotated datasets. [Expand]

Tuesday Poster Session

Shiyu Tan, Yicheng Wu, Shoou-I Yu, Ashok Veeraraghavan

Conventional stereo suffers from a fundamental trade-off between imaging volume and signal-to-noise ratio (SNR) -- due to the conflicting impact of aperture size on both these variables. [Expand]

Wednesday Poster Session

Mengyue Yang, Furui Liu, Zhitang Chen, Xinwei Shen, Jianye Hao, Jun Wang

Learning disentanglement aims at finding a low dimensional representation which consists of multiple explanatory and generative factors of the observational data. [Expand]

Wednesday Poster Session

Yanbei Chen, Yongqin Xian, A. Sophia Koepke, Ying Shan, Zeynep Akata

Having access to multi-modal cues (e.g. [Expand]

Tuesday Poster Session

Mohamed Hassan, Partha Ghosh, Joachim Tesch, Dimitrios Tzionas, Michael J. Black

Humans live within a 3D space and constantly interact with it to perform tasks. [Expand]

Thursday Poster Session

Shamit Lal, Mihir Prabhudesai, Ishita Mediratta, Adam W. Harley, Katerina Fragkiadaki

This paper explores self-supervised learning of amodal 3D feature representations from RGB and RGB-D posed images and videos, agnostic to object and scene semantic content, and evaluates the resulting scene representations in the downstream tasks of visual correspondence, object tracking, and object detection. [Expand]

Thursday Poster Session

Abulikemu Abuduweili, Xingjian Li, Humphrey Shi, Cheng-Zhong Xu, Dejing Dou

While recent studies on semi-supervised learning have shown remarkable progress in leveraging both labeled and unlabeled data, most of them presume a basic setting of the model is randomly initialized. [Expand]

Tuesday Poster Session

Kai Zhang, Fujun Luan, Qianqian Wang, Kavita Bala, Noah Snavely

We present an end-to-end inverse rendering pipeline that includes a fully differentiable renderer, and can reconstruct geometry, materials, and illumination from scratch from a set of images. [Expand]

Tuesday Poster Session

Antoine Miech, Jean-Baptiste Alayrac, Ivan Laptev, Josef Sivic, Andrew Zisserman

Our objective is language-based search of large-scale image and video datasets. [Expand]

Wednesday Poster Session

Mohammed Suhail, Abhay Mittal, Behjat Siddiquie, Chris Broaddus, Jayan Eledath, Gerard Medioni, Leonid Sigal

Traditional scene graph generation methods are trained using cross-entropy losses that treat objects and relationships as independent entities. [Expand]

Thursday Poster Session

Hongsuk Choi, Gyeongsik Moon, Ju Yong Chang, Kyoung Mu Lee

Despite the recent success of single image-based 3D human pose and shape estimation methods, recovering temporally consistent and smooth 3D human motion from a video is still challenging. [Expand]

Monday Poster Session

Radek Mackowiak, Lynton Ardizzone, Ullrich Kothe, Carsten Rother

With the maturing of deep learning systems, trustworthiness is becoming increasingly important for model assessment. [Expand]

Tuesday Poster Session

Guangting Wang, Yizhou Zhou, Chong Luo, Wenxuan Xie, Wenjun Zeng, Zhiwei Xiong

Inspired by the fact that human eyes continue to develop tracking ability in early and middle childhood, we propose to use tracking as a proxy task for a computer vision system to learn the visual representations. [Expand]

Monday Poster Session

Gregory P. Meyer

The Huber loss is a robust loss function used for a wide range of regression tasks. [Expand]

Tuesday Poster Session

Xingran Zhou, Bo Zhang, Ting Zhang, Pan Zhang, Jianmin Bao, Dong Chen, Zhongfei Zhang, Fang Wen

We present the full-resolution correspondence learning for cross-domain images, which aids image translation. [Expand]

Thursday Poster Session

Ruojin Cai, Bharath Hariharan, Noah Snavely, Hadar Averbuch-Elor

We present a technique for estimating the relative 3D rotation of an RGB image pair in an extreme setting, where the images have little or no overlap. [Expand]

Thursday Poster Session

Brent A. Griffin, Jason J. Corso

This paper addresses the problem of learning to estimate the depth of detected objects given some measurement of camera motion (e.g., from robot kinematics or vehicle odometry). [Expand]

Monday Poster Session

Qibin Hou, Daquan Zhou, Jiashi Feng

Recent studies on mobile network design have demonstrated the remarkable effectiveness of channel attention (e.g., the Squeeze-and-Excitation attention) for lifting model performance, but they generally neglect the positional information, which is important for generating spatially selective attention maps. [Expand]

Thursday Poster Session

Arianna Rampini, Franco Pestarini, Luca Cosmo, Simone Melzi, Emanuele Rodola

Machine learning models are known to be vulnerable to adversarial attacks, namely perturbations of the data that lead to wrong predictions despite being imperceptible. [Expand]

Tuesday Poster Session

Yunxuan Wei, Shuhang Gu, Yawei Li, Radu Timofte, Longcun Jin, Hengjie Song

These days, unsupervised super-resolution (SR) is soaring due to its practical and promising potential in real scenarios. [Expand]

Thursday Poster Session

Francis Engelmann, Konstantinos Rematas, Bastian Leibe, Vittorio Ferrari

We propose a method to detect and reconstruct multiple 3D objects from a single RGB image. [Expand]

Tuesday Poster Session

Hugo Germain, Vincent Lepetit, Guillaume Bourmaud

Absolute camera pose estimation is usually addressed by sequentially solving two distinct subproblems: First a feature matching problem that seeks to establish putative 2D-3D correspondences, and then a Perspective-n-Point problem that minimizes, w.r.t. [Expand]

Monday Poster Session

Yunhua Zhang, Ling Shao, Cees G. M. Snoek

This paper strives for repetitive activity counting in videos. [Expand]

Thursday Poster Session

Haiyan Wu, Yanyun Qu, Shaohui Lin, Jian Zhou, Ruizhi Qiao, Zhizhong Zhang, Yuan Xie, Lizhuang Ma

Single image dehazing is a challenging ill-posed problem due to the severe information degeneration. [Expand]

Wednesday Poster Session

Gengshan Yang, Deva Ramanan

Appearance-based detectors achieve remarkable performance on common scenes, benefiting from high-capacity models and massive annotated data, but tend to fail for scenarios that lack training data. [Expand]

Monday Poster Session

Mingi Ji, Seungjae Shin, Seunghyun Hwang, Gibeom Park, Il-Chul Moon

Knowledge distillation is a method of transferring the knowledge from a pretrained complex teacher model to a student model, so a smaller network can replace a large teacher network at the deployment stage. [Expand]

Wednesday Poster Session

Tian Pan, Yibing Song, Tianyu Yang, Wenhao Jiang, Wei Liu

MoCo is effective for unsupervised image representation learning. [Expand]

Wednesday Poster Session

Jiaqi Wang, Wenwei Zhang, Yuhang Zang, Yuhang Cao, Jiangmiao Pang, Tao Gong, Kai Chen, Ziwei Liu, Chen Change Loy, Dahua Lin

Instance segmentation has witnessed a remarkable progress on class-balanced benchmarks. [Expand]

Wednesday Poster Session

Ze Yang, Shenlong Wang, Sivabalan Manivasagam, Zeng Huang, Wei-Chiu Ma, Xinchen Yan, Ersin Yumer, Raquel Urtasun

Constructing and animating humans is an important component for building virtual worlds in a wide variety of applications such as virtual reality or robotics testing in simulation. [Expand]

Thursday Poster Session

Tianlong Chen, Jonathan Frankle, Shiyu Chang, Sijia Liu, Yang Zhang, Michael Carbin, Zhangyang Wang

The computer vision world has been re-gaining enthusiasm in various pre-trained models, including both classical ImageNet supervised pre-training and recently emerged self-supervised pre-training such as simCLR and MoCo. [Expand]

Friday Poster Session

Riccardo Volpi, Diane Larlus, Gregory Rogez

Most standard learning approaches lead to fragile models which are prone to drift when sequentially trained on samples of a different nature -- the well-known "catastrophic forgetting" issue. [Expand]

Tuesday Poster Session

Edward Zhang, Ricardo Martin-Brualla, Janne Kontkanen, Brian L. Curless

Removing objects from images is a challenging technical problem that is important for many applications, including mixed reality. [Expand]

Friday Poster Session

Mehmet Aygun, Aljosa Osep, Mark Weber, Maxim Maximov, Cyrill Stachniss, Jens Behley, Laura Leal-Taixe

Temporal semantic scene understanding is critical for self-driving cars or robots operating in dynamic environments. [Expand]

Tuesday Poster Session

Soravit Changpinyo, Piyush Sharma, Nan Ding, Radu Soricut

The availability of large-scale image captioning and visual question answering datasets has contributed significantly to recent successes in vision-and-language pre-training. [Expand]

Tuesday Poster Session

Bindita Chaudhuri, Nikolaos Sarafianos, Linda Shapiro, Tony Tung

We introduce a novel approach to generate diverse high fidelity texture maps for 3D human meshes in a semi-supervised setup. [Expand]

Wednesday Poster Session

Corentin Kervadec, Grigory Antipov, Moez Baccouche, Christian Wolf

Models for Visual Question Answering (VQA) are notorious for their tendency to rely on dataset biases, as the large and unbalanced diversity of questions and concepts involved and tends to prevent models from learning to ""reason"", leading them to perform ""educated guesses"" instead. [Expand]

Monday Poster Session

Song-Hai Zhang, Yuan-Chen Guo, Qing-Wen Gu

We investigate the problem of generating 3D meshes from single free-hand sketches, aiming at fast 3D modeling for novice users. [Expand]

Tuesday Poster Session

Yinan He, Bei Gan, Siyu Chen, Yichun Zhou, Guojun Yin, Luchuan Song, Lu Sheng, Jing Shao, Ziwei Liu

The rapid progress of photorealistic synthesis techniques has reached at a critical point where the boundary between real and manipulated images starts to blur. [Expand]

Tuesday Poster Session

Yandong Li, Xuhui Jia, Ruoxin Sang, Yukun Zhu, Bradley Green, Liqiang Wang, Boqing Gong

This paper is concerned with ranking many pre-trained deep neural networks (DNNs), called checkpoints, for the transfer learning to a downstream task. [Expand]

Monday Poster Session

Weihao Xia, Yujiu Yang, Jing-Hao Xue, Baoyuan Wu

In this work, we propose TediGAN, a novel framework for multi-modal image generation and manipulation with textual descriptions. [Expand]

Monday Poster Session

Yuqian Zhou, David Ren, Neil Emerton, Sehoon Lim, Timothy Large

The new trend of full-screen devices encourages us to position a camera behind a screen. [Expand]

Wednesday Poster Session

Yuxiao Zhou, Marc Habermann, Ikhsanul Habibie, Ayush Tewari, Christian Theobalt, Feng Xu

We present the first method for real-time full body capture that estimates shape and motion of body and hands together with a dynamic 3D face model from a single color image. [Expand]

Tuesday Poster Session

Qunjie Zhou, Torsten Sattler, Laura Leal-Taixe

The classical matching pipeline used for visual localization typically involves three steps: (i) local feature detection and description, (ii) feature matching, and (iii) outlier rejection. [Expand]

Tuesday Poster Session

Jaedong Hwang, Seoung Wug Oh, Joon-Young Lee, Bohyung Han

We extend panoptic segmentation to the open-world and introduce an open-set panoptic segmentation (OPS) task. [Expand]

Monday Poster Session

Moritz Kappel, Vladislav Golyanik, Mohamed Elgharib, Jann-Ole Henningson, Hans-Peter Seidel, Susana Castillo, Christian Theobalt, Marcus Magnor

Video-based human motion transfer creates video animations of humans following a source motion. [Expand]

Monday Poster Session

Xiang Li, Wenhai Wang, Xiaolin Hu, Jun Li, Jinhui Tang, Jian Yang

Localization Quality Estimation (LQE) is crucial and popular in the recent advancement of dense object detectors since it can provide accurate ranking scores that benefit the Non-Maximum Suppression processing and improve detection performance. [Expand]

Thursday Poster Session

Zhongzheng Ren, Ishan Misra, Alexander G. Schwing, Rohit Girdhar

We introduce WyPR, a Weakly-supervised framework for Point cloud Recognition, requiring only scene-level class tags as supervision. [Expand]

Thursday Poster Session

Yang Zhao, Changyou Chen

Image-to-image translation aims to preserve source contents while translating to discriminative target styles between two visual domains. [Expand]

Friday Poster Session

Arda Duzceker, Silvano Galliani, Christoph Vogel, Pablo Speciale, Mihai Dusmanu, Marc Pollefeys

We propose an online multi-view depth prediction approach on posed video streams, where the scene geometry information computed in the previous time steps is propagated to the current time step in an efficient and geometrically plausible way. [Expand]

Thursday Poster Session

Tarun Yenamandra, Ayush Tewari, Florian Bernard, Hans-Peter Seidel, Mohamed Elgharib, Daniel Cremers, Christian Theobalt

We present the first deep implicit 3D morphable model (i3DMM) of full heads. [Expand]

Thursday Poster Session

Sumith Kulal, Jiayuan Mao, Alex Aiken, Jiajun Wu

Current approaches to video analysis of human motion focus on raw pixels or keypoints as the basic units of reasoning. [Expand]

Tuesday Poster Session

Qiang Meng, Shichao Zhao, Zhida Huang, Feng Zhou

The performance of face recognition system degrades when the variability of the acquired faces increases. [Expand]

Thursday Poster Session

Chulin Xie, Chuxin Wang, Bo Zhang, Hao Yang, Dong Chen, Fang Wen

In this paper, we proposed a novel Style-based Point Generator with Adversarial Rendering (SpareNet) for point cloud completion. [Expand]

Tuesday Poster Session

Mohamed El Banani, Luya Gao, Justin Johnson

Aligning partial views of a scene into a single whole is essential to understanding one's environment and is a key component of numerous robotics tasks such as SLAM and SfM. [Expand]

Wednesday Poster Session

Amit Raj, Julian Tanke, James Hays, Minh Vo, Carsten Stoll, Christoph Lassner

Deferred Neural Rendering (DNR) uses a three-step pipeline to translate a mesh representation into an RGB image. [Expand]

Tuesday Poster Session

Xinlong Wang, Rufeng Zhang, Chunhua Shen, Tao Kong, Lei Li

To date, most existing self-supervised learning methods are designed and optimized for image classification. [Expand]

Tuesday Poster Session

Abhishek Badki, Orazio Gallo, Jan Kautz, Pradeep Sen

Time-to-contact (TTC), the time for an object to collide with the observer's plane, is a powerful tool for path planning: it is potentially more informative than the depth, velocity, and acceleration of objects in the scene---even for humans. [Expand]

Thursday Poster Session

Udaranga Wickramasinghe, Pascal Fua, Graham Knott

Active Surface Models have a long history of being useful to model complex 3D surfaces. [Expand]

Thursday Poster Session

Xudong Lin, Gedas Bertasius, Jue Wang, Shih-Fu Chang, Devi Parikh, Lorenzo Torresani

We present Vx2Text, a framework for text generation from multimodal inputs consisting of video plus text, speech, or audio. [Expand]

Tuesday Poster Session

Qihang Yu, Jianming Zhang, He Zhang, Yilin Wang, Zhe Lin, Ning Xu, Yutong Bai, Alan Yuille

We propose Mask Guided (MG) Matting, a robust matting framework that takes a general coarse mask as guidance. [Expand]

Monday Poster Session

Nikita Durasov, Timur Bagautdinov, Pierre Baque, Pascal Fua

Deep neural networks have amply demonstrated their prowess but estimating the reliability of their predictions remains challenging. [Expand]

Thursday Poster Session

Berk Kaya, Suryansh Kumar, Carlos Oliveira, Vittorio Ferrari, Luc Van Gool

This paper presents an uncalibrated deep neural network framework for the photometric stereo problem. [Expand]

Tuesday Poster Session

Muhammad Ferjad Naeem, Yongqin Xian, Federico Tombari, Zeynep Akata

In compositional zero-shot learning, the goal is to recognize unseen compositions (e.g. [Expand]

Monday Poster Session

Jevgenij Gamper, Nasir Rajpoot

We present ARCH, a computational pathology (CP) multiple instance captioning dataset to facilitate dense supervision of CP tasks. [Expand]

Friday Poster Session

Guillaume Jaume, Pushpak Pati, Behzad Bozorgtabar, Antonio Foncubierta, Anna Maria Anniciello, Florinda Feroce, Tilman Rau, Jean-Philippe Thiran, Maria Gabrani, Orcun Goksel

Explainability of deep learning methods is imperative to facilitate their clinical adoption in digital pathology. [Expand]

Wednesday Poster Session

Zhengang Li, Geng Yuan, Wei Niu, Pu Zhao, Yanyu Li, Yuxuan Cai, Xuan Shen, Zheng Zhan, Zhenglun Kong, Qing Jin, Zhiyu Chen, Sijia Liu, Kaiyuan Yang, Bin Ren, Yanzhi Wang, Xue Lin

With the increasing demand to efficiently deploy DNNs on mobile edge devices, it becomes much more important to reduce unnecessary computation and increase the execution speed. [Expand]

Thursday Poster Session

Jialun Peng, Dong Liu, Songcen Xu, Houqiang Li

Given an incomplete image without additional constraint, image inpainting natively allows for multiple solutions as long as they appear plausible. [Expand]

Wednesday Poster Session

Chuhan Zhang, Ankush Gupta, Andrew Zisserman

Our objective in this work is fine-grained classification of actions in untrimmed videos, where the actions may be temporally extended or may span only a few frames of the video. [Expand]

Tuesday Poster Session

Liangyu Chen, Tong Yang, Xiangyu Zhang, Wei Zhang, Jian Sun

We propose a novel point annotated setting for the weakly semi-supervised object detection task, in which the dataset comprises small fully annotated images and large weakly annotated images by points. [Expand]

Wednesday Poster Session

Linus Ericsson, Henry Gouk, Timothy M. Hospedales

Self-supervised visual representation learning has seen huge progress recently, but no large scale evaluation has compared the many models now available. [Expand]

Tuesday Poster Session

Suman Saha, Anton Obukhov, Danda Pani Paudel, Menelaos Kanakis, Yuhua Chen, Stamatios Georgoulis, Luc Van Gool

We present an approach for encoding visual task relationships to improve model performance in an Unsupervised Domain Adaptation (UDA) setting. [Expand]

Wednesday Poster Session

Rohit Saha, Brendan Duke, Florian Shkurti, Graham W. Taylor, Parham Aarabi

Hairstyle transfer is challenging due to hair structure differences in the source and target hair. [Expand]

Monday Poster Session

Luca Weihs, Matt Deitke, Aniruddha Kembhavi, Roozbeh Mottaghi

There has been a significant recent progress in the field of Embodied AI with researchers developing models and algorithms enabling embodied agents to navigate and interact within completely unseen environments. [Expand]

Tuesday Poster Session

Shariq Farooq Bhat, Ibraheem Alhashim, Peter Wonka

We address the problem of estimating a high quality dense depth map from a single RGB input image. [Expand]

Tuesday Poster Session

Denis Gudovskiy, Luca Rigazio, Shun Ishizaka, Kazuki Kozuka, Sotaro Tsukizawa

AutoAugment has sparked an interest in automated augmentation methods for deep learning models. [Expand]

Friday Poster Session

Zijian Hu, Zhengyu Yang, Xuefeng Hu, Ram Nevatia

A common classification task situation is where one has a large amount of data available for training, but only a small portion is annotated with class labels. [Expand]

Thursday Poster Session

Austin Stone, Daniel Maurer, Alper Ayvaci, Anelia Angelova, Rico Jonschkowski

We present SMURF, a method for unsupervised learning of optical flow that improves state of the art on all benchmarks by 36% to 40% and even outperforms several supervised approaches such as PWC-Net and FlowNet2. [Expand]

Tuesday Poster Session

Zhenda Xie, Yutong Lin, Zheng Zhang, Yue Cao, Stephen Lin, Han Hu

Contrastive learning methods for unsupervised visual representation learning have reached remarkable levels of transfer performance. [Expand]

Friday Poster Session

Zheng Zhu, Guan Huang, Jiankang Deng, Yun Ye, Junjie Huang, Xinze Chen, Jiagang Zhu, Tian Yang, Jiwen Lu, Dalong Du, Jie Zhou

In this paper, we contribute a new million-scale face benchmark containing noisy 4M identities/260M faces (WebFace260M) and cleaned 2M identities/42M faces (WebFace42M) training data, as well as an elaborately designed time-constrained evaluation protocol. [Expand]

Wednesday Poster Session

Razvan Caramalau, Binod Bhattarai, Tae-Kyun Kim

We propose a novel pool-based Active Learning frame-work constructed on a sequential Graph Convolution Net-work (GCN). [Expand]

Wednesday Poster Session

Julian Chibane, Aayush Bansal, Verica Lazova, Gerard Pons-Moll

Recent neural view synthesis methods have achieved impressive quality and realism, surpassing classical pipelines which rely on multi-view reconstruction. [Expand]

Wednesday Poster Session

Zhichao Huang, Xintong Han, Jia Xu, Tong Zhang

We present a new method for few-shot human motion transfer that achieves realistic human image generation with only a small number of appearance inputs. [Expand]

Monday Poster Session

Bumsoo Kim, Junhyun Lee, Jaewoo Kang, Eun-Sol Kim, Hyunwoo J. Kim

Human-Object Interaction (HOI) detection is a task of identifying "a set of interactions" in an image, which involves the i) localization of the subject (i.e., humans) and target (i.e., objects) of interaction, and ii) the classification of the interaction labels. [Expand]

Monday Poster Session

Byungseok Roh, Wuhyun Shin, Ildoo Kim, Sungwoong Kim

Self-supervised learning has been widely used to obtain transferrable representations from unlabeled images. [Expand]

Monday Poster Session

Gowri Somanath, Daniel Kurz

We present a method to estimate an HDR environment map from a narrow field-of-view LDR camera image in real-time. [Expand]

Wednesday Poster Session

Jong-Chyi Su, Zezhou Cheng, Subhransu Maji

We evaluate the effectiveness of semi-supervised learning (SSL) on a realistic benchmark where data exhibits considerable class imbalance and contains images from novel classes. [Expand]

Thursday Poster Session

Kevin C. Zhou, Colin Cooke, Jaehee Park, Ruobing Qian, Roarke Horstmeyer, Joseph A. Izatt, Sina Farsiu

We present a feature-free photogrammetric technique that enables quantitative 3D mesoscopic (mm-scale height variation) imaging with tens-of-micron accuracy from sequences of images acquired by a smartphone at close range (several cm) under freehand motion without additional hardware. [Expand]

Wednesday Poster Session

Erik Franz, Barbara Solenthaler, Nils Thuerey

We propose a novel method to reconstruct volumetric flows from sparse views via a global transport formulation. [Expand]

Monday Poster Session

Yinyu Nie, Ji Hou, Xiaoguang Han, Matthias Niessner

Semantic scene understanding from point clouds is particularly challenging as the points reflect only a sparse set of the underlying 3D geometry. [Expand]

Tuesday Poster Session

Yifan Wang, Andrew Liu, Richard Tucker, Jiajun Wu, Brian L. Curless, Steven M. Seitz, Noah Snavely

We present a framework for automatically reconfiguring images of street scenes by populating, depopulating, or repopulating them with objects such as pedestrians or vehicles. [Expand]

Tuesday Poster Session

Andrew Hou, Ze Zhang, Michel Sarkis, Ning Bi, Yiying Tong, Xiaoming Liu

Existing face relighting methods often struggle with two problems: maintaining the local facial details of the subject and accurately removing and synthesizing shadows in the relit image, especially hard shadows. [Expand]

Thursday Poster Session

Kenneth Marino, Xinlei Chen, Devi Parikh, Abhinav Gupta, Marcus Rohrbach

One of the most challenging question types in VQA is when answering the question requires outside knowledge not present in the image. [Expand]

Thursday Poster Session

Yu Mitsuzumi, Go Irie, Daiki Ikami, Takashi Shibata

Many variants of unsupervised domain adaptation (UDA) problems have been proposed and solved individually. [Expand]

Monday Poster Session

Zhiqin Chen, Vladimir G. Kim, Matthew Fisher, Noam Aigerman, Hao Zhang, Siddhartha Chaudhuri

We introduce a deep generative network for 3D shape detailization, akin to stylization with the style being geometric details. [Expand]

Friday Poster Session

Yujun Shi, Li Yuan, Yunpeng Chen, Jiashi Feng

Continual learning tackles the setting of learning different tasks sequentially. [Expand]

Friday Poster Session

Li Yi, Boqing Gong, Thomas Funkhouser

We study an unsupervised domain adaptation problem for the semantic labeling of 3D point clouds, with a particular focus on domain discrepancies induced by different LiDAR sensors. [Expand]

Thursday Poster Session

Mohamed Abdelsalam, Mojtaba Faramarzi, Shagun Sodhani, Sarath Chandar

We introduce the 'Incremental Implicitly-Refined Classification (IIRC)' setup, an extension to the class incremental learning setup where the incoming batches of classes have two granularity levels. [Expand]

Wednesday Poster Session

Byeong-Uk Lee, Kyunghyun Lee, In So Kweon

The basic framework of depth completion is to predict a pixel-wise dense depth map using very sparse input data. [Expand]

Thursday Poster Session

Weiyang Liu, Rongmei Lin, Zhen Liu, James M. Rehg, Liam Paull, Li Xiong, Le Song, Adrian Weller

The inductive bias of a neural network is largely determined by the architecture and the training algorithm. [Expand]

Wednesday Poster Session

Xintao Wang, Yu Li, Honglun Zhang, Ying Shan

Blind face restoration usually relies on facial priors, such as facial geometry prior or reference prior, to restore realistic and faithful details. [Expand]

Wednesday Poster Session

Ceyuan Yang, Zhirong Wu, Bolei Zhou, Stephen Lin

Prior research on self-supervised learning has led to considerable progress on image classification, but often with degraded transfer performance on object detection. [Expand]

Tuesday Poster Session

Mohammadreza Salehi, Niousha Sadjadi, Soroosh Baselizadeh, Mohammad H. Rohban, Hamid R. Rabiee

Unsupervised representation learning has proved to be a critical component of anomaly detection/localization in images. [Expand]

Thursday Poster Session

Sheng Ao, Qingyong Hu, Bo Yang, Andrew Markham, Yulan Guo

Extracting robust and general 3D local features is key to downstream tasks such as point cloud registration and reconstruction. [Expand]

Thursday Poster Session

Junzhe Zhang, Xinyi Chen, Zhongang Cai, Liang Pan, Haiyu Zhao, Shuai Yi, Chai Kiat Yeo, Bo Dai, Chen Change Loy

Most 3D shape completion approaches rely heavily on partial-complete shape pairs and learn in a fully supervised manner. [Expand]

Monday Poster Session

Cheng Zou, Bohan Wang, Yue Hu, Junqi Liu, Qian Wu, Yu Zhao, Boxun Li, Chenguang Zhang, Chi Zhang, Yichen Wei, Jian Sun

We propose HOI Transformer to tackle human object interaction (HOI) detection in an end-to-end manner. [Expand]

Thursday Poster Session

Ayan Kumar Bhunia, Pinaki Nath Chowdhury, Yongxin Yang, Timothy M. Hospedales, Tao Xiang, Yi-Zhe Song

Self-supervised learning has gained prominence due to its efficacy at learning powerful representations from unlabelled data that achieve excellent performance on many challenging downstream tasks. [Expand]

Tuesday Poster Session

Yu-Wei Chao, Wei Yang, Yu Xiang, Pavlo Molchanov, Ankur Handa, Jonathan Tremblay, Yashraj S. Narang, Karl Van Wyk, Umar Iqbal, Stan Birchfield, Jan Kautz, Dieter Fox

We introduce DexYCB, a new dataset for capturing hand grasping of objects. [Expand]

Wednesday Poster Session

Shaofei Wang, Andreas Geiger, Siyu Tang

Registering point clouds of dressed humans to parametric human models is a challenging task in computer vision. [Expand]

Wednesday Poster Session

Yan Xu, Yu-Jhe Li, Xinshuo Weng, Kris Kitani

We address the problem of estimating the 3D pose of a network of cameras for large-environment wide-baseline scenarios, e.g., cameras for construction sites, sports stadiums, and public spaces. [Expand]

Thursday Poster Session

Amanda Duarte, Shruti Palaskar, Lucas Ventura, Deepti Ghadiyaram, Kenneth DeHaan, Florian Metze, Jordi Torres, Xavier Giro-i-Nieto

One of the factors that have hindered progress in the areas of sign language recognition, translation, and production is the absence of large annotated datasets. [Expand]

Monday Poster Session

Priyanka Patel, Chun-Hao P. Huang, Joachim Tesch, David T. Hoffmann, Shashank Tripathi, Michael J. Black

While the accuracy of 3D human pose estimation from images has steadily improved on benchmark datasets, the best methods still fail in many real-world scenarios. [Expand]

Thursday Poster Session

Siyuan Qiao, Yukun Zhu, Hartwig Adam, Alan Yuille, Liang-Chieh Chen

In this paper, we present ViP-DeepLab, a unified model attempting to tackle the long-standing and challenging inverse projection problem in vision, which we model as restoring the point clouds from perspective image sequences while providing each point with instance-level semantic interpretations. [Expand]

Tuesday Poster Session

Bo Sun, Banghuai Li, Shengcai Cai, Ye Yuan, Chi Zhang

Emerging interests have been brought to recognize previously unseen objects given very few training examples, known as few-shot object detection (FSOD). [Expand]

Wednesday Poster Session

Yichun Shi, Divyansh Aggarwal, Anil K. Jain

We propose a framework, called LiftedGAN, that disentangles and lifts a pre-trained StyleGAN2 for 3D-aware face generation. [Expand]

Tuesday Poster Session

Jiayu Yang, Jose M. Alvarez, Miaomiao Liu

Recent supervised multi-view depth estimation networks have achieved promising results. [Expand]

Wednesday Poster Session

Luca Schmidtke, Athanasios Vlontzos, Simon Ellershaw, Anna Lukens, Tomoki Arichi, Bernhard Kainz

Human pose estimation is a major computer vision problem with applications ranging from augmented reality and video capture to surveillance and movement tracking. [Expand]

Monday Poster Session

Fangzhou Hong, Hui Zhou, Xinge Zhu, Hongsheng Li, Ziwei Liu

With the rapid advances of autonomous driving, it becomes critical to equip its sensing system with more holistic 3D perception. [Expand]

Thursday Poster Session

Tianwei Lin, Zhuoqi Ma, Fu Li, Dongliang He, Xin Li, Errui Ding, Nannan Wang, Jie Li, Xinbo Gao

Artistic style transfer aims at migrating the style from an example image to a content image. [Expand]

Tuesday Poster Session

Luca Morreale, Noam Aigerman, Vladimir G. Kim, Niloy J. Mitra

Maps are arguably one of the most fundamental concepts used to define and operate on manifold surfaces in differentiable geometry. [Expand]

Tuesday Poster Session

Wolfgang Stammer, Patrick Schramowski, Kristian Kersting

Most explanation methods in deep learning map importance estimates for a model's prediction back to the original input space. [Expand]

Tuesday Poster Session

Mianlun Zheng, Yi Zhou, Duygu Ceylan, Jernej Barbic

Fast and light-weight methods for animating 3D characters are desirable in various applications such as computer games. [Expand]

Tuesday Poster Session

Yuchen Liu, Zhixin Shu, Yijun Li, Zhe Lin, Federico Perazzi, Sun-Yuan Kung

Generative adversarial networks (GANs), e.g., StyleGAN2, play a vital role in various image generation and synthesis tasks, yet their notoriously high computational cost hinders their efficient deployment on edge devices. [Expand]

Thursday Poster Session

Youjiang Xu, Linchao Zhu, Lu Jiang, Yi Yang

It has been shown that deep neural networks are prone to overfitting on biased training data. [Expand]

Monday Poster Session

Yang Hong, Juyong Zhang, Boyi Jiang, Yudong Guo, Ligang Liu, Hujun Bao

In this paper, we propose StereoPIFu, which integrates the geometric constraints of stereo vision with implicit function representation of PIFu, to recover the 3D shape of the clothed human from a pair of low-cost rectified images. [Expand]

Monday Poster Session

Fabio Pizzati, Pietro Cerri, Raoul de Charette

CoMoGAN is a continuous GAN relying on the unsupervised reorganization of the target data on a functional manifold. [Expand]

Thursday Poster Session

Ziyuan Huang, Shiwei Zhang, Jianwen Jiang, Mingqian Tang, Rong Jin, Marcelo H. Ang

Motions are reflected in videos as the movement of pixels, and actions are essentially patterns of inconsistent motions between the foreground and the background. [Expand]

Monday Poster Session

Soo Ye Kim, Hyeonjun Sim, Munchurl Kim

Blind super-resolution (SR) methods aim to generate a high quality high resolution image from a low resolution image containing unknown degradations. [Expand]

Wednesday Poster Session

Yuda Qiu, Xiaojie Xu, Lingteng Qiu, Yan Pan, Yushuang Wu, Weikai Chen, Xiaoguang Han

Caricature is an artistic representation that deliberately exaggerates the distinctive features of a human face to convey humor or sarcasm. [Expand]

Wednesday Poster Session

Saquib Sarfraz, Naila Murray, Vivek Sharma, Ali Diba, Luc Van Gool, Rainer Stiefelhagen

Action segmentation refers to inferring boundaries of semantically consistent visual concepts in videos and is an important requirement for many video understanding tasks. [Expand]

Wednesday Poster Session

Yujiao Shi, Hongdong Li, Xin Yu

We address the problem of novel view synthesis (NVS) from a few sparse source view images. [Expand]

Wednesday Poster Session

Xiao Song, Guorun Yang, Xinge Zhu, Hui Zhou, Zhe Wang, Jianping Shi

Recently, records on stereo matching benchmarks are constantly broken by end-to-end disparity networks. [Expand]

Wednesday Poster Session

Mingyang Zhou, Luowei Zhou, Shuohang Wang, Yu Cheng, Linjie Li, Zhou Yu, Jingjing Liu

Vision-and-language pre-training has achieved impressive success in learning multimodal representations between vision and language. [Expand]

Tuesday Poster Session

Yuval Nirkin, Lior Wolf, Tal Hassner

We present a novel, real-time, semantic segmentation network in which the encoder both encodes and generates the parameters (weights) of the decoder. [Expand]

Tuesday Poster Session

Jingtan Piao, Keqiang Sun, Quan Wang, Kwan-Yee Lin, Hongsheng Li

Given a monocular face image as input, 3D face geometry reconstruction aims to recover a corresponding 3Dface mesh. [Expand]

Friday Poster Session

Cody Reading, Ali Harakeh, Julia Chae, Steven L. Waslander

Monocular 3D object detection is a key problem for autonomous vehicles, as it provides a solution with simple configuration compared to typical multi-sensor systems. [Expand]

Wednesday Poster Session

Mallikarjun B R, Ayush Tewari, Tae-Hyun Oh, Tim Weyrich, Bernd Bickel, Hans-Peter Seidel, Hanspeter Pfister, Wojciech Matusik, Mohamed Elgharib, Christian Theobalt

The reflectance field of a face describes the reflectance properties responsible for complex lighting effects including diffuse, specular, inter-reflection and self shadowing. [Expand]

Tuesday Poster Session

Pei Wang, Yijun Li, Krishna Kumar Singh, Jingwan Lu, Nuno Vasconcelos

Synthesizing variations of a specific reference image with semantically valid content is an important task in terms of personalized generation as well as for data augmentation. [Expand]

Tuesday Poster Session

Mingchen Zhuge, Dehong Gao, Deng-Ping Fan, Linbo Jin, Ben Chen, Haoming Zhou, Minghui Qiu, Ling Shao

We present a new vision-language (VL) pre-training model dubbed Kaleido-BERT, which introduces a novel kaleido strategy for fashion cross-modality representations from transformers. [Expand]

Thursday Poster Session

Minheng Ni, Haoyang Huang, Lin Su, Edward Cui, Taroon Bharti, Lijuan Wang, Dongdong Zhang, Nan Duan

We present M3P, a Multitask Multilingual Multimodal Pre-trained model that combines multilingual pre-training and multimodal pre-training into a unified framework via multitask pre-training. [Expand]