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November 6, 2021 Rami Aly, Zhijiang Guo, Michael Schlichtkrull, James Thorne, Andreas Vlachos, Christos Christodoulopoulos, Oana Cocarascu, Arpit Mittal
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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.
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October 20, 2021 Sachin Mehta, Amit Kumar, Fitsum Reda, Varun Nasery, Vikram Mulukutla, Rakesh Ranjan, Vikas Chandra
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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.
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October 12, 2021 Pingchuan Ma, Rodrigo Mira, Stavros Petridis, Björn W. Schuller, Maja Pantic
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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.
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October 11, 2021 Bo Xiong, Haoqi Fan, Kristen Grauman, Christoph Feichtenhofer
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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.
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October 11, 2021 Hao Jiang, Vamsi Krishna Ithapu
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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.
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October 11, 2021 Garvita Tiwari, Nikolaos Sarafianos, Tony Tung, Gerard Pons-Moll
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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.
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October 11, 2021 Nathan Inkawhich, Kevin J Liang, Jingyang Zhang, Huanrui Yang, Hai Li, Yiran Chen
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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.
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October 11, 2021 Yash Kant, Abhinav Moudgil, Dhruv Batra, Devi Parikh, Harsh Agrawal
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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.
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October 10, 2021 Ben Graham, Alaaeldin El-Nouby, Hugo Touvron, Pierre Stock, Armand Joulin, Hervé Jégou, Matthijs Douze
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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.
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October 4, 2021 Kai-En Lin, Lei Xiao, Feng Liu, Guowei Yang, Ravi Ramamoorthi
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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.
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