280 citations · 666 across the 12 of their papers we have counts for
14 papers
Where Should I Spend My FLOPS? Efficiency Evaluations of Visual Pre-training Methods
Skanda Koppula, Yazhe Li, Evan Shelhamer +5
Self-supervised methods have achieved remarkable success in transfer learning, often achieving the same or better accuracy than supervised pre-training. Most prior work has done so…
HiP: Hierarchical Perceiver
Joao Carreira, Skanda Koppula, Daniel Zoran +10
General perception systems such as Perceivers can process arbitrary modalities in any combination and are able to handle up to a few hundred thousand inputs. They achieve this gene…
Compact Deep Aggregation for Set Retrieval
Yujie Zhong, Relja Arandjelović, Andrew Zisserman
The objective of this work is to learn a compact embedding of a set of descriptors that is suitable for efficient retrieval and ranking, whilst maintaining discriminability of the…
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…
Neighbourhood Consensus Networks
Ignacio Rocco, Mircea Cimpoi, Relja Arandjelović +3
We address the problem of finding reliable dense correspondences between a pair of images. This is a challenging task due to strong appearance differences between the corresponding…
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…
On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models
Sven Gowal, Krishnamurthy Dvijotham, Robert Stanforth +6
Recent work has shown that it is possible to train deep neural networks that are provably robust to norm-bounded adversarial perturbations. Most of these methods are based on minim…
Training verified learners with learned verifiers
Krishnamurthy Dvijotham, Sven Gowal, Robert Stanforth +4
This paper proposes a new algorithmic framework, predictor-verifier training, to train neural networks that are verifiable, i.e., networks that provably satisfy some desired input-…
End-to-end weakly-supervised semantic alignment
Ignacio Rocco, Relja Arandjelović, Josef Sivic
We tackle the task of semantic alignment where the goal is to compute dense semantic correspondence aligning two images depicting objects of the same category. This is a challengin…
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…
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…
Convolutional neural network architecture for geometric matching
Ignacio Rocco, Relja Arandjelović, Josef Sivic
We address the problem of determining correspondences between two images in agreement with a geometric model such as an affine or thin-plate spline transformation, and estimating i…
Pairwise Quantization
Artem Babenko, Relja Arandjelović, Victor Lempitsky
We consider the task of lossy compression of high-dimensional vectors through quantization. We propose the approach that learns quantization parameters by minimizing the distortion…
NetVLAD: CNN architecture for weakly supervised place recognition
Relja Arandjelović, Petr Gronat, Akihiko Torii +2
We tackle the problem of large scale visual place recognition, where the task is to quickly and accurately recognize the location of a given query photograph. We present the follow…