546 citations · 1.3k across the 25 of their papers we have counts for
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The Monocular Depth Estimation Challenge
Jaime Spencer, C. Stella Qian, Chris Russell +16
This paper summarizes the results of the first Monocular Depth Estimation Challenge (MDEC) organized at WACV2023. This challenge evaluated the progress of self-supervised monocular…
Deconstructing Self-Supervised Monocular Reconstruction: The Design Decisions that Matter
Jaime Spencer, Chris Russell, Simon Hadfield +1
This paper presents an open and comprehensive framework to systematically evaluate state-of-the-art contributions to self-supervised monocular depth estimation. This includes pretr…
Pixel-level Correspondence for Self-Supervised Learning from Video
Yash Sharma, Yi Zhu, Chris Russell +1
While self-supervised learning has enabled effective representation learning in the absence of labels, for vision, video remains a relatively untapped source of supervision. To add…
Assaying Out-Of-Distribution Generalization in Transfer Learning
Florian Wenzel, Andrea Dittadi, Peter Vincent Gehler +9
Since out-of-distribution generalization is a generally ill-posed problem, various proxy targets (e.g., calibration, adversarial robustness, algorithmic corruptions, invariance acr…
"The Pedestrian next to the Lamppost" Adaptive Object Graphs for Better Instantaneous Mapping
Avishkar Saha, Oscar Mendez, Chris Russell +1
Estimating a semantically segmented bird's-eye-view (BEV) map from a single image has become a popular technique for autonomous control and navigation. However, they show an increa…
Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers
Dominik Zietlow, Michael Lohaus, Guha Balakrishnan +4
Algorithmic fairness is frequently motivated in terms of a trade-off in which overall performance is decreased so as to improve performance on disadvantaged groups where the algori…