4 papers · 1 filter
Adaptive MLP Pruning for Large Vision Transformers
Chengchao Shen
Large vision transformers present impressive scalability, as their performance can be well improved with increased model capacity. Nevertheless, their cumbersome parameters results…
Hallucination Begins Where Saliency Drops
Xiaofeng Zhang, Yuanchao Zhu, Chaochen Gu +8
Recent studies have examined attention dynamics in large vision-language models (LVLMs) to detect hallucinations. However, existing approaches remain limited in reliably distinguis…
Learning Compact Vision Tokens for Efficient Large Multimodal Models
Hao Tang, Chengchao Shen
Large multimodal models (LMMs) suffer significant computational challenges due to the high cost of Large Language Models (LLMs) and the quadratic complexity of processing long visi…
Diversity-Guided MLP Reduction for Efficient Large Vision Transformers
Chengchao Shen, Hourun Zhu, Gongfan Fang +2
Transformer models achieve excellent scaling property, where the performance is improved with the increment of model capacity. However, large-scale model parameters lead to an unaf…