75.5k citations · 100.8k across the 160 of their papers we have counts for
30 papers · 1 filter
Video Action Transformer Network
Rohit Girdhar, João Carreira, Carl Doersch +1
We introduce the Action Transformer model for recognizing and localizing human actions in video clips. We repurpose a Transformer-style architecture to aggregate features from the…
The Visual Centrifuge: Model-Free Layered Video Representations
Jean-Baptiste Alayrac, João Carreira, Andrew Zisserman
True video understanding requires making sense of non-lambertian scenes where the color of light arriving at the camera sensor encodes information about not just the last object it…
Deep Frank-Wolfe For Neural Network Optimization
Leonard Berrada, Andrew Zisserman, M. Pawan Kumar
Learning a deep neural network requires solving a challenging optimization problem: it is a high-dimensional, non-convex and non-smooth minimization problem with a large number of…
Class-Agnostic Counting
Erika Lu, Weidi Xie, Andrew Zisserman
Nearly all existing counting methods are designed for a specific object class. Our work, however, aims to create a counting model able to count any class of object. To achieve this…
LRS3-TED: a large-scale dataset for visual speech recognition
Triantafyllos Afouras, Joon Son Chung, Andrew Zisserman
This paper introduces a new multi-modal dataset for visual and audio-visual speech recognition. It includes face tracks from over 400 hours of TED and TEDx videos, along with the c…
GhostVLAD for set-based face recognition
Yujie Zhong, Relja Arandjelović, Andrew Zisserman
The objective of this paper is to learn a compact representation of image sets for template-based face recognition. We make the following contributions: first, we propose a network…
3D Surface Reconstruction by Pointillism
Olivia Wiles, Andrew Zisserman
The objective of this work is to infer the 3D shape of an object from a single image. We use sculptures as our training and test bed, as these have great variety in shape and appea…
Turning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network Embeddings
Mohsan Alvi, Andrew Zisserman, Christoffer Nellaker
Neural networks achieve the state-of-the-art in image classification tasks. However, they can encode spurious variations or biases that may be present in the training data. For exa…
Learning to Read by Spelling: Towards Unsupervised Text Recognition
Ankush Gupta, Andrea Vedaldi, Andrew Zisserman
This work presents a method for visual text recognition without using any paired supervisory data. We formulate the text recognition task as one of aligning the conditional distrib…
From Same Photo: Cheating on Visual Kinship Challenges
Mitchell Dawson, Andrew Zisserman, Christoffer Nellåker
With the propensity for deep learning models to learn unintended signals from data sets there is always the possibility that the network can `cheat' in order to solve a task. In th…
Deep Audio-Visual Speech Recognition
Triantafyllos Afouras, Joon Son Chung, Andrew Senior +2
The goal of this work is to recognise phrases and sentences being spoken by a talking face, with or without the audio. Unlike previous works that have focussed on recognising a lim…
Self-supervised learning of a facial attribute embedding from video
Olivia Wiles, A. Sophia Koepke, Andrew Zisserman
We propose a self-supervised framework for learning facial attributes by simply watching videos of a human face speaking, laughing, and moving over time. To perform this task, we i…
Emotion Recognition in Speech using Cross-Modal Transfer in the Wild
Samuel Albanie, Arsha Nagrani, Andrea Vedaldi +1
Obtaining large, human labelled speech datasets to train models for emotion recognition is a notoriously challenging task, hindered by annotation cost and label ambiguity. In this…
A Short Note about Kinetics-600
Joao Carreira, Eric Noland, Andras Banki-Horvath +2
We describe an extension of the DeepMind Kinetics human action dataset from 400 classes, each with at least 400 video clips, to 600 classes, each with at least 600 video clips. In…
Comparator Networks
Weidi Xie, Li Shen, Andrew Zisserman
The objective of this work is set-based verification, e.g. to decide if two sets of images of a face are of the same person or not. The traditional approach to this problem is to l…
X2Face: A network for controlling face generation by using images, audio, and pose codes
Olivia Wiles, A. Sophia Koepke, Andrew Zisserman
The objective of this paper is a neural network model that controls the pose and expression of a given face, using another face or modality (e.g. audio). This model can then be use…
A Better Baseline for AVA
Rohit Girdhar, João Carreira, Carl Doersch +1
We introduce a simple baseline for action localization on the AVA dataset. The model builds upon the Faster R-CNN bounding box detection framework, adapted to operate on pure spati…
Multicolumn Networks for Face Recognition
Weidi Xie, Andrew Zisserman
The objective of this work is set-based face recognition, i.e. to decide if two sets of images of a face are of the same person or not. Conventionally, the set-wise feature descrip…
Inductive Visual Localisation: Factorised Training for Superior Generalisation
Ankush Gupta, Andrea Vedaldi, Andrew Zisserman
End-to-end trained Recurrent Neural Networks (RNNs) have been successfully applied to numerous problems that require processing sequences, such as image captioning, machine transla…
VoxCeleb2: Deep Speaker Recognition
Joon Son Chung, Arsha Nagrani, Andrew Zisserman
The objective of this paper is speaker recognition under noisy and unconstrained conditions. We make two key contributions. First, we introduce a very large-scale audio-visual spea…
Deep Lip Reading: a comparison of models and an online application
Triantafyllos Afouras, Joon Son Chung, Andrew Zisserman
The goal of this paper is to develop state-of-the-art models for lip reading -- visual speech recognition. We develop three architectures and compare their accuracy and training ti…
Massively Parallel Video Networks
Joao Carreira, Viorica Patraucean, Laurent Mazare +2
We introduce a class of causal video understanding models that aims to improve efficiency of video processing by maximising throughput, minimising latency, and reducing the number…
Learnable PINs: Cross-Modal Embeddings for Person Identity
Arsha Nagrani, Samuel Albanie, Andrew Zisserman
We propose and investigate an identity sensitive joint embedding of face and voice. Such an embedding enables cross-modal retrieval from voice to face and from face to voice. We ma…
Seeing Voices and Hearing Faces: Cross-modal biometric matching
Arsha Nagrani, Samuel Albanie, Andrew Zisserman
We introduce a seemingly impossible task: given only an audio clip of someone speaking, decide which of two face images is the speaker. In this paper we study this, and a number of…