3 citations · 5 across the 14 of their papers we have counts for
4 papers · 1 filter
Bifocal: Training-Free Mixed Granularity Visual Token Pruning for Multimodal LLMs
Peiran Yin, Hao Wu, Zihao Yang +6
Visual token pruning for multimodal large language models (MLLMs) appears highly effective, with existing methods retaining over 95% of unpruned performance on common VQA benchmark…
UTPTrack: Towards Simple and Unified Token Pruning for Visual Tracking
Hao Wu, Xudong Wang, Jialiang Zhang +5
One-stream Transformer-based trackers achieve advanced performance in visual object tracking but suffer from significant computational overhead that hinders real-time deployment. W…
ViCA: Efficient Multimodal LLMs with Vision-Only Cross-Attention
Wenjie Liu, Hao Wu, Xin Qiu +6
Modern multimodal large language models (MLLMs) adopt a unified self-attention design that processes visual and textual tokens at every Transformer layer, incurring substantial com…
Visually Prompted Benchmarks Are Surprisingly Fragile
Haiwen Feng, Long Lian, Lisa Dunlap +6
A key challenge in evaluating VLMs is testing models' ability to analyze visual content independently from their textual priors. Recent benchmarks such as BLINK probe visual percep…