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20172022
most citedData Augmentation Can Improve Robustness

13 citations · 14 across the 4 of their papers we have counts for

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10 papers · 1 filter

cs.CV2022

Compressed Vision for Efficient Video Understanding

Olivia Wiles, Joao Carreira, Iain Barr +2

Experience and reasoning occur across multiple temporal scales: milliseconds, seconds, hours or days. The vast majority of computer vision research, however, still focuses on indiv…

cs.CV202113 cited

Data Augmentation Can Improve Robustness

Sylvestre-Alvise Rebuffi, Sven Gowal, Dan A. Calian +3

Adversarial training suffers from robust overfitting, a phenomenon where the robust test accuracy starts to decrease during training. In this paper, we focus on reducing robust ove…

cs.CV2021

Fixing Data Augmentation to Improve Adversarial Robustness

Sylvestre-Alvise Rebuffi, Sven Gowal, Dan A. Calian +3

Adversarial training suffers from robust overfitting, a phenomenon where the robust test accuracy starts to decrease during training. In this paper, we focus on both heuristics-dri…

cs.CV2020

Co-Attention for Conditioned Image Matching

Olivia Wiles, Sebastien Ehrhardt, Andrew Zisserman

We propose a new approach to determine correspondences between image pairs in the wild under large changes in illumination, viewpoint, context, and material. While other approaches…

cs.CV2019

SynSin: End-to-end View Synthesis from a Single Image

Olivia Wiles, Georgia Gkioxari, Richard Szeliski +1

Single image view synthesis allows for the generation of new views of a scene given a single input image. This is challenging, as it requires comprehensively understanding the 3D s…

cs.CV2019

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…