3.5k citations · 9.4k across the 54 of their papers we have counts for
63 papers
FACTS: First Amplify Correlations and Then Slice to Discover Bias
Sriram Yenamandra, Pratik Ramesh, Viraj Prabhu +1
Computer vision datasets frequently contain spurious correlations between task-relevant labels and (easy to learn) latent task-irrelevant attributes (e.g. context). Models trained…
Memory in Plain Sight: Surveying the Uncanny Resemblances of Associative Memories and Diffusion Models
Benjamin Hoover, Hendrik Strobelt, Dmitry Krotov +3
The generative process of Diffusion Models (DMs) has recently set state-of-the-art on many AI generation benchmarks. Though the generative process is traditionally understood as an…
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…
LANCE: Stress-testing Visual Models by Generating Language-guided Counterfactual Images
Viraj Prabhu, Sriram Yenamandra, Prithvijit Chattopadhyay +1
We propose an automated algorithm to stress-test a trained visual model by generating language-guided counterfactual test images (LANCE). Our method leverages recent progress in la…
ZipIt! Merging Models from Different Tasks without Training
George Stoica, Daniel Bolya, Jakob Bjorner +3
Typical deep visual recognition models are capable of performing the one task they were trained on. In this paper, we tackle the extremely difficult problem of combining distinct m…