2.4k citations · 3.1k across the 7 of their papers we have counts for
7 papers
ICON: Reliably Benchmarking Predictive Inequity in Object Detection
Sruthi Sudhakar, Viraj Prabhu, Olga Russakovsky +1
As computer vision systems are being increasingly deployed at scale in high-stakes applications like autonomous driving, concerns about social bias in these systems are rising. Ana…
Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles
Chaitanya Ryali, Yuan-Ting Hu, Daniel Bolya +10
Modern hierarchical vision transformers have added several vision-specific components in the pursuit of supervised classification performance. While these components lead to effect…
Token Merging for Fast Stable Diffusion
Daniel Bolya, Judy Hoffman
The landscape of image generation has been forever changed by open vocabulary diffusion models. However, at their core these models use transformers, which makes generation slow. B…
Bridging the Sim2Real gap with CARE: Supervised Detection Adaptation with Conditional Alignment and Reweighting
Viraj Prabhu, David Acuna, Andrew Liao +5
Sim2Real domain adaptation (DA) research focuses on the constrained setting of adapting from a labeled synthetic source domain to an unlabeled or sparsely labeled real target domai…
Mitigating Bias in Visual Transformers via Targeted Alignment
Sruthi Sudhakar, Viraj Prabhu, Arvindkumar Krishnakumar +1
As transformer architectures become increasingly prevalent in computer vision, it is critical to understand their fairness implications. We perform the first study of the fairness…
FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation
Judy Hoffman, Dequan Wang, Fisher Yu +1
Fully convolutional models for dense prediction have proven successful for a wide range of visual tasks. Such models perform well in a supervised setting, but performance can be su…