no code implementations • ICCV 2023 • Laura Gustafson, Chloe Rolland, Nikhila Ravi, Quentin Duval, Aaron Adcock, Cheng-Yang Fu, Melissa Hall, Candace Ross
We present a new benchmark named FACET (FAirness in Computer Vision EvaluaTion), a large, publicly available evaluation set of 32k images for some of the most common vision tasks - image classification, object detection and segmentation.
1 code implementation • 11 Apr 2023 • Laura Gustafson, Megan Richards, Melissa Hall, Caner Hazirbas, Diane Bouchacourt, Mark Ibrahim
As an example, we show that mitigating a model's vulnerability to texture can improve performance on the lower income level.
18 code implementations • ICCV 2023 • Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dollár, Ross Girshick
We introduce the Segment Anything (SA) project: a new task, model, and dataset for image segmentation.
Ranked #2 on Zero-Shot Instance Segmentation on LVIS v1.0 val
no code implementations • 16 Feb 2023 • Melissa Hall, Bobbie Chern, Laura Gustafson, Denisse Ventura, Harshad Kulkarni, Candace Ross, Nicolas Usunier
These metrics successfully incentivized performance improvements on person-centric tasks such as face analysis and are used to understand risks of modern models.
no code implementations • 26 Jan 2023 • Melissa Hall, Laura Gustafson, Aaron Adcock, Ishan Misra, Candace Ross
With these capabilities in mind, we ask: Do vision-language models exhibit gender bias when performing zero-shot image classification, object detection and semantic segmentation?
1 code implementation • 27 Jan 2022 • Melissa Hall, Laurens van der Maaten, Laura Gustafson, Maxwell Jones, Aaron Adcock
To enable this study, we design a simple image-classification problem in which we can tightly control (synthetic) biases.
2 code implementations • CVPR 2022 • Mannat Singh, Laura Gustafson, Aaron Adcock, Vinicius de Freitas Reis, Bugra Gedik, Raj Prateek Kosaraju, Dhruv Mahajan, Ross Girshick, Piotr Dollár, Laurens van der Maaten
Model pre-training is a cornerstone of modern visual recognition systems.
Ranked #1 on Out-of-Distribution Generalization on ImageNet-W (using extra training data)
Fine-Grained Image Classification Out-of-Distribution Generalization +3
1 code implementation • 18 Jun 2020 • Rohit Girdhar, Laura Gustafson, Aaron Adcock, Laurens van der Maaten
Physical reasoning requires forward prediction: the ability to forecast what will happen next given some initial world state.
Ranked #2 on Visual Reasoning on PHYRE-1B-Within
2 code implementations • NeurIPS 2019 • Anton Bakhtin, Laurens van der Maaten, Justin Johnson, Laura Gustafson, Ross Girshick
The benchmark is designed to encourage the development of learning algorithms that are sample-efficient and generalize well across puzzles.
Ranked #3 on Visual Reasoning on PHYRE-1B-Within
1 code implementation • ICML 2020 • Gregory Farquhar, Laura Gustafson, Zeming Lin, Shimon Whiteson, Nicolas Usunier, Gabriel Synnaeve
In complex tasks, such as those with large combinatorial action spaces, random exploration may be too inefficient to achieve meaningful learning progress.