562 citations · 615 across the 10 of their papers we have counts for
6 papers · 1 filter
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…
Pinpointing Why Object Recognition Performance Degrades Across Income Levels and Geographies
Laura Gustafson, Megan Richards, Melissa Hall +3
Despite impressive advances in object-recognition, deep learning systems' performance degrades significantly across geographies and lower income levels raising pressing concerns of…
Segment Anything
Alexander Kirillov, Eric Mintun, Nikhila Ravi +9
We introduce the Segment Anything (SA) project: a new task, model, and dataset for image segmentation. Using our efficient model in a data collection loop, we built the largest seg…
Towards Reliable Assessments of Demographic Disparities in Multi-Label Image Classifiers
Melissa Hall, Bobbie Chern, Laura Gustafson +4
Disaggregated performance metrics across demographic groups are a hallmark of fairness assessments in computer vision. These metrics successfully incentivized performance improveme…
Vision-Language Models Performing Zero-Shot Tasks Exhibit Gender-based Disparities
Melissa Hall, Laura Gustafson, Aaron Adcock +2
We explore the extent to which zero-shot vision-language models exhibit gender bias for different vision tasks. Vision models traditionally required task-specific labels for repres…
Revisiting Weakly Supervised Pre-Training of Visual Perception Models
Mannat Singh, Laura Gustafson, Aaron Adcock +7
Model pre-training is a cornerstone of modern visual recognition systems. Although fully supervised pre-training on datasets like ImageNet is still the de-facto standard, recent st…