4 citations · 8 across the 18 of their papers we have counts for
6 papers · 1 filter
Feather the Throttle: Revisiting Visual Token Pruning for Vision-Language Model Acceleration
Mark Endo, Xiaohan Wang, Serena Yeung-Levy
Recent works on accelerating Vision-Language Models achieve strong performance across a variety of vision-language tasks despite highly compressing visual information. In this work…
Zero-shot Action Localization via the Confidence of Large Vision-Language Models
Josiah Aklilu, Xiaohan Wang, Serena Yeung-Levy
Precise action localization in untrimmed video is vital for fields such as professional sports and minimally invasive surgery, where the delineation of particular motions in record…
Video-STaR: Self-Training Enables Video Instruction Tuning with Any Supervision
Orr Zohar, Xiaohan Wang, Yonatan Bitton +2
The performance of Large Vision Language Models (LVLMs) is dependent on the size and quality of their training datasets. Existing video instruction tuning datasets lack diversity a…
Why are Visually-Grounded Language Models Bad at Image Classification?
Yuhui Zhang, Alyssa Unell, Xiaohan Wang +4
Image classification is one of the most fundamental capabilities of machine vision intelligence. In this work, we revisit the image classification task using visually-grounded lang…
VideoAgent: Long-form Video Understanding with Large Language Model as Agent
Xiaohan Wang, Yuhui Zhang, Orr Zohar +1
Long-form video understanding represents a significant challenge within computer vision, demanding a model capable of reasoning over long multi-modal sequences. Motivated by the hu…
Just Shift It: Test-Time Prototype Shifting for Zero-Shot Generalization with Vision-Language Models
Elaine Sui, Xiaohan Wang, Serena Yeung-Levy
Advancements in vision-language models (VLMs) have propelled the field of computer vision, particularly in the zero-shot learning setting. Despite their promise, the effectiveness…