85 citations · 113 across the 15 of their papers we have counts for
5 papers · 1 filter
In-Context Learning Can Help Vision-Language Models Overcome Training Priors
Kun Wang, Xindi Wu, Sanghyuk Chun +2
Vision-language models (VLMs) learn strong statistical regularities during training, which can make them fail to properly respond to visual evidence when input images violate those…
Personalized Generative Models for Contextual Debiasing
Xinran Liang, Esin Tureci, Prachi Sinha +3
Different visual patterns appear with different frequencies in the world: e.g., beach balls appear on sand more often than they do on a road. These statistics are reflected in visi…
Bias at the End of the Score
Salma Abdel Magid, Grace Guo, Esin Tureci +4
Reward models (RMs) are inherently non-neutral value functions designed and trained to encode specific objectives, such as human preferences or text-image alignment. RMs have becom…
Beyond Objects: Contextual Synthetic Data Generation for Fine-Grained Classification
William Yang, Xindi Wu, Zhiwei Deng +2
Text-to-image (T2I) models are increasingly used for synthetic dataset generation, but generating effective synthetic training data for classification remains challenging. Fine-tun…
Visual Compositional Tuning
Xindi Wu, Hee Seung Hwang, Polina Kirichenko +2
Visual instruction tuning (VIT) datasets have grown rapidly in scale, yet the informativeness of individual training samples has largely been overlooked. Recent dataset selection m…