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November 6, 2021
Rami Aly, Zhijiang Guo, Michael Schlichtkrull, James Thorne, Andreas Vlachos, Christos Christodoulopoulos, Oana Cocarascu, Arpit Mittal
FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured information
In this paper we introduce a novel dataset and benchmark, Fact Extraction and VERification Over Unstructured and Structured information (FEVEROUS), which consists of 87,026 verified claims.
Areas
ARTIFICIAL INTELLIGENCE
MACHINE LEARNING
Paper
October 20, 2021
Sachin Mehta, Amit Kumar, Fitsum Reda, Varun Nasery, Vikram Mulukutla, Rakesh Ranjan, Vikas Chandra
EVRNet: Efficient Video Restoration on Edge Devices
In video transmission applications, video signals are transmitted over lossy channels, resulting in low-quality received signals. To restore videos on recipient edge devices in real-time, we introduce an efficient video restoration network, EVRNet.
Areas
COMPUTATIONAL PHOTOGRAPHY & INTELLIGENT CAMERAS
COMPUTER VISION
MACHINE LEARNING
Paper
October 12, 2021
Pingchuan Ma, Rodrigo Mira, Stavros Petridis, Björn W. Schuller, Maja Pantic
LiRA: Learning Visual Speech Representations from Audio through Self-supervision
In this work, we propose Learning visual speech Representations from Audio via self-supervision (LiRA). Specifically, we train a ResNet+Conformer model to predict acoustic features from unlabelled visual speech.
Areas
COMPUTER VISION
MACHINE LEARNING
Paper
October 11, 2021
Hao Jiang, Vamsi Krishna Ithapu
Egocentric Pose Estimation from Human Vision Span
In this paper, we tackle the egopose estimation from a more natural human vision span, where camera wearer can be seen in the peripheral view and depending on the head pose the wearer may become invisible or has a limited partial view.
Areas
AR/VR
COMPUTER VISION
MACHINE LEARNING

Paper
October 11, 2021
Garvita Tiwari, Nikolaos Sarafianos, Tony Tung, Gerard Pons-Moll
Neural-GIF: Neural Generalized Implicit Functions for Animating People in Clothing
We present Neural Generalized Implicit Functions (Neural-GIF), to animate people in clothing as a function of the body pose. Given a sequence of scans of a subject in various poses, we learn to animate the character for new poses.
Areas
AR/VR
COMPUTER VISION
MACHINE LEARNING

Paper
October 11, 2021
Nathan Inkawhich, Kevin J Liang, Jingyang Zhang, Huanrui Yang, Hai Li, Yiran Chen
Can Targeted Adversarial Examples Transfer When the Source and Target Models Have No Label Space Overlap?
We design blackbox transfer-based targeted adversarial attacks for an environment where the attacker’s source model and the target blackbox model may have disjoint label spaces and training datasets. This scenario significantly differs from the “standard” blackbox setting, and warrants a unique approach to the attacking process. Our methodology begins with the construction of a class correspondence matrix between the whitebox and blackbox label sets.
Areas
COMPUTER VISION
MACHINE LEARNING
Paper
October 11, 2021
Yash Kant, Abhinav Moudgil, Dhruv Batra, Devi Parikh, Harsh Agrawal
Contrast and Classify: Training Robust VQA Models
We propose a novel training paradigm (ConClaT) that optimizes both cross-entropy and contrastive losses. The contrastive loss encourages representations to be robust to linguistic variations in questions while the cross-entropy loss preserves the discriminative power of representations for answer prediction.
Areas
ARTIFICIAL INTELLIGENCE
COMPUTER VISION
MACHINE LEARNING
NATURAL LANGUAGE PROCESSING & SPEECH
Paper
October 11, 2021
Bo Xiong, Haoqi Fan, Kristen Grauman, Christoph Feichtenhofer
Multiview Pseudo-Labeling for Semi-supervised Learning from Video
Though our method capitalizes on multiple views, it nonetheless trains a model that is shared across appearance and motion input and thus, by design, incurs no additional computation overhead at inference time.
Areas
ARTIFICIAL INTELLIGENCE
COMPUTER VISION
MACHINE LEARNING
Paper
October 10, 2021
Ben Graham, Alaaeldin El-Nouby, Hugo Touvron, Pierre Stock, Armand Joulin, Hervé Jégou, Matthijs Douze
LeViT: a Vision Transformer in ConvNet’s Clothing for Faster Inference
We design a family of image classification architectures that optimize the trade-off between accuracy and efficiency in a high-speed regime. Our work exploits recent findings in attention-based architectures, which are competitive on highly parallel processing hardware.
Areas
ARTIFICIAL INTELLIGENCE
COMPUTER VISION
MACHINE LEARNING
Paper
October 4, 2021
Kai-En Lin, Lei Xiao, Feng Liu, Guowei Yang, Ravi Ramamoorthi
Deep 3D Mask Volume for View Synthesis of Dynamic Scenes
We develop a new algorithm, Deep 3D Mask Volume, which enables temporally stable view extrapolation from binocular videos of dynamic scenes, captured by static cameras.
Areas
AR/VR
COMPUTATIONAL PHOTOGRAPHY & INTELLIGENT CAMERAS
COMPUTER VISION
MACHINE LEARNING
Paper
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