75.5k citations · 100.8k across the 160 of their papers we have counts for
12 papers · 1 filter
Objects that Sound
Relja Arandjelović, Andrew Zisserman
In this paper our objectives are, first, networks that can embed audio and visual inputs into a common space that is suitable for cross-modal retrieval; and second, a network that…
SilNet : Single- and Multi-View Reconstruction by Learning from Silhouettes
Olivia Wiles, Andrew Zisserman
The objective of this paper is 3D shape understanding from single and multiple images. To this end, we introduce a new deep-learning architecture and loss function, SilNet, that ca…
VGGFace2: A dataset for recognising faces across pose and age
Qiong Cao, Li Shen, Weidi Xie +2
In this paper, we introduce a new large-scale face dataset named VGGFace2. The dataset contains 3.31 million images of 9131 subjects, with an average of 362.6 images for each subje…
Detect to Track and Track to Detect
Christoph Feichtenhofer, Axel Pinz, Andrew Zisserman
Recent approaches for high accuracy detection and tracking of object categories in video consist of complex multistage solutions that become more cumbersome each year. In this pape…
Multi-task Self-Supervised Visual Learning
Carl Doersch, Andrew Zisserman
We investigate methods for combining multiple self-supervised tasks--i.e., supervised tasks where data can be collected without manual labeling--in order to train a single visual r…
Self-Supervised Learning for Spinal MRIs
Amir Jamaludin, Timor Kadir, Andrew Zisserman
A significant proportion of patients scanned in a clinical setting have follow-up scans. We show in this work that such longitudinal scans alone can be used as a form of 'free' sel…
Temporal HeartNet: Towards Human-Level Automatic Analysis of Fetal Cardiac Screening Video
Weilin Huang, Christopher P. Bridge, J. Alison Noble +1
We present an automatic method to describe clinically useful information about scanning, and to guide image interpretation in ultrasound (US) videos of the fetal heart. Our method…
VoxCeleb: a large-scale speaker identification dataset
Arsha Nagrani, Joon Son Chung, Andrew Zisserman
Most existing datasets for speaker identification contain samples obtained under quite constrained conditions, and are usually hand-annotated, hence limited in size. The goal of th…
The Kinetics Human Action Video Dataset
Will Kay, Joao Carreira, Karen Simonyan +9
We describe the DeepMind Kinetics human action video dataset. The dataset contains 400 human action classes, with at least 400 video clips for each action. Each clip lasts around 1…
Look, Listen and Learn
Relja Arandjelović, Andrew Zisserman
We consider the question: what can be learnt by looking at and listening to a large number of unlabelled videos? There is a valuable, but so far untapped, source of information con…
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
Joao Carreira, Andrew Zisserman
The paucity of videos in current action classification datasets (UCF-101 and HMDB-51) has made it difficult to identify good video architectures, as most methods obtain similar per…
You said that?
Joon Son Chung, Amir Jamaludin, Andrew Zisserman
We present a method for generating a video of a talking face. The method takes as inputs: (i) still images of the target face, and (ii) an audio speech segment; and outputs a video…