75.5k citations · 100.8k across the 160 of their papers we have counts for
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VoxSRC 2019: The first VoxCeleb Speaker Recognition Challenge
Joon Son Chung, Arsha Nagrani, Ernesto Coto +4
The VoxCeleb Speaker Recognition Challenge 2019 aimed to assess how well current speaker recognition technology is able to identify speakers in unconstrained or `in the wild' data.…
End-to-End Learning of Visual Representations from Uncurated Instructional Videos
Antoine Miech, Jean-Baptiste Alayrac, Lucas Smaira +3
Annotating videos is cumbersome, expensive and not scalable. Yet, many strong video models still rely on manually annotated data. With the recent introduction of the HowTo100M data…
Synthetic Humans for Action Recognition from Unseen Viewpoints
Gül Varol, Ivan Laptev, Cordelia Schmid +1
Although synthetic training data has been shown to be beneficial for tasks such as human pose estimation, its use for RGB human action recognition is relatively unexplored. Our goa…
ASR is all you need: cross-modal distillation for lip reading
Triantafyllos Afouras, Joon Son Chung, Andrew Zisserman
The goal of this work is to train strong models for visual speech recognition without requiring human annotated ground truth data. We achieve this by distilling from an Automatic S…
Self-supervised learning of class embeddings from video
Olivia Wiles, A. Sophia Koepke, Andrew Zisserman
This work explores how to use self-supervised learning on videos to learn a class-specific image embedding that encodes pose and shape information. At train time, two frames of the…
Controllable Attention for Structured Layered Video Decomposition
Jean-Baptiste Alayrac, João Carreira, Relja Arandjelović +1
The objective of this paper is to be able to separate a video into its natural layers, and to control which of the separated layers to attend to. For example, to be able to separat…
Count, Crop and Recognise: Fine-Grained Recognition in the Wild
Max Bain, Arsha Nagrani, Daniel Schofield +1
The goal of this paper is to label all the animal individuals present in every frame of a video. Unlike previous methods that have principally concentrated on labelling face tracks…
Video Representation Learning by Dense Predictive Coding
Tengda Han, Weidi Xie, Andrew Zisserman
The objective of this paper is self-supervised learning of spatio-temporal embeddings from video, suitable for human action recognition. We make three contributions: First, we intr…
Geometry-Aware Video Object Detection for Static Cameras
Dan Xu, Weidi Xie, Andrew Zisserman
In this paper we propose a geometry-aware model for video object detection. Specifically, we consider the setting that cameras can be well approximated as static, e.g. in video sur…
Learning to Discover Novel Visual Categories via Deep Transfer Clustering
Kai Han, Andrea Vedaldi, Andrew Zisserman
We consider the problem of discovering novel object categories in an image collection. While these images are unlabelled, we also assume prior knowledge of related but different im…
EPIC-Fusion: Audio-Visual Temporal Binding for Egocentric Action Recognition
Evangelos Kazakos, Arsha Nagrani, Andrew Zisserman +1
We focus on multi-modal fusion for egocentric action recognition, and propose a novel architecture for multi-modal temporal-binding, i.e. the combination of modalities within a ran…
AutoCorrect: Deep Inductive Alignment of Noisy Geometric Annotations
Honglie Chen, Weidi Xie, Andrea Vedaldi +1
We propose AutoCorrect, a method to automatically learn object-annotation alignments from a dataset with annotations affected by geometric noise. The method is based on a consisten…
My lips are concealed: Audio-visual speech enhancement through obstructions
Triantafyllos Afouras, Joon Son Chung, Andrew Zisserman
Our objective is an audio-visual model for separating a single speaker from a mixture of sounds such as other speakers and background noise. Moreover, we wish to hear the speaker e…
Use What You Have: Video Retrieval Using Representations From Collaborative Experts
Yang Liu, Samuel Albanie, Arsha Nagrani +1
The rapid growth of video on the internet has made searching for video content using natural language queries a significant challenge. Human-generated queries for video datasets `i…
A Short Note on the Kinetics-700 Human Action Dataset
Joao Carreira, Eric Noland, Chloe Hillier +1
We describe an extension of the DeepMind Kinetics human action dataset from 600 classes to 700 classes, where for each class there are at least 600 video clips from different YouTu…
Sim2real transfer learning for 3D human pose estimation: motion to the rescue
Carl Doersch, Andrew Zisserman
Synthetic visual data can provide practically infinite diversity and rich labels, while avoiding ethical issues with privacy and bias. However, for many tasks, current models train…
LAEO-Net: revisiting people Looking At Each Other in videos
Manuel J. Marin-Jimenez, Vicky Kalogeiton, Pablo Medina-Suarez +1
Capturing the `mutual gaze' of people is essential for understanding and interpreting the social interactions between them. To this end, this paper addresses the problem of detecti…
Unsupervised Learning of Object Keypoints for Perception and Control
Tejas Kulkarni, Ankush Gupta, Catalin Ionescu +4
The study of object representations in computer vision has primarily focused on developing representations that are useful for image classification, object detection, or semantic s…
Training Neural Networks for and by Interpolation
Leonard Berrada, Andrew Zisserman, M. Pawan Kumar
In modern supervised learning, many deep neural networks are able to interpolate the data: the empirical loss can be driven to near zero on all samples simultaneously. In this work…
A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities
Simon A. A. Kohl, Bernardino Romera-Paredes, Klaus H. Maier-Hein +5
Medical imaging only indirectly measures the molecular identity of the tissue within each voxel, which often produces only ambiguous image evidence for target measures of interest,…
Object Discovery with a Copy-Pasting GAN
Relja Arandjelović, Andrew Zisserman
We tackle the problem of object discovery, where objects are segmented for a given input image, and the system is trained without using any direct supervision whatsoever. A novel c…
Exploiting temporal context for 3D human pose estimation in the wild
Anurag Arnab, Carl Doersch, Andrew Zisserman
We present a bundle-adjustment-based algorithm for recovering accurate 3D human pose and meshes from monocular videos. Unlike previous algorithms which operate on single frames, we…
Semi-Supervised Learning with Scarce Annotations
Sylvestre-Alvise Rebuffi, Sebastien Ehrhardt, Kai Han +2
While semi-supervised learning (SSL) algorithms provide an efficient way to make use of both labelled and unlabelled data, they generally struggle when the number of annotated samp…
A Geometric Approach to Obtain a Bird's Eye View from an Image
Ammar Abbas, Andrew Zisserman
The objective of this paper is to rectify any monocular image by computing a homography matrix that transforms it to a bird's eye (overhead) view. We make the following contributio…