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20222024
most citedVision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision

48 citations · 114 across the 9 of their papers we have counts for

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Showing 2023 · cs.CVShow all

5 papers · 2 filters

cs.CV2023★ 16 cited

Emu Video: Factorizing Text-to-Video Generation by Explicit Image Conditioning

Rohit Girdhar, Mannat Singh, Andrew Brown +7

We present Emu Video, a text-to-video generation model that factorizes the generation into two steps: first generating an image conditioned on the text, and then generating a video…

cs.CV2023★ 1 cited

FACET: Fairness in Computer Vision Evaluation Benchmark

Laura Gustafson, Chloe Rolland, Nikhila Ravi +5

Computer vision models have known performance disparities across attributes such as gender and skin tone. This means during tasks such as classification and detection, model perfor…

cs.CV2023★ 3 cited

The effectiveness of MAE pre-pretraining for billion-scale pretraining

Mannat Singh, Quentin Duval, Kalyan Vasudev Alwala +9

This paper revisits the standard pretrain-then-finetune paradigm used in computer vision for visual recognition tasks. Typically, state-of-the-art foundation models are pretrained…

cs.CV2023★ 3 cited

A Simple Recipe for Competitive Low-compute Self supervised Vision Models

Quentin Duval, Ishan Misra, Nicolas Ballas

Self-supervised methods in vision have been mostly focused on large architectures as they seem to suffer from a significant performance drop for smaller architectures. In this pape…

cs.CV2023★ 19 cited

Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture

Mahmoud Assran, Quentin Duval, Ishan Misra +5

This paper demonstrates an approach for learning highly semantic image representations without relying on hand-crafted data-augmentations. We introduce the Image-based Joint-Embedd…