3 papers
cs.CV2025
EM-KD: Distilling Efficient Multimodal Large Language Model with Unbalanced Vision Tokens
Ze Feng, Sen Yang, Boqiang Duan +2
Efficient Multimodal Large Language Models (MLLMs) compress vision tokens to reduce resource consumption, but the loss of visual information can degrade comprehension capabilities.…
cs.CV2025
Vision Remember: Recovering Visual Information in Efficient LVLM with Vision Feature Resampling
Ze Feng, Jiang-jiang Liu, Sen Yang +5
The computational expense of redundant vision tokens in Large Vision-Language Models (LVLMs) has led many existing methods to compress them via a vision projector. However, this co…
cs.CV2024
Revisiting MLLMs: An In-Depth Analysis of Image Classification Abilities
Huan Liu, Lingyu Xiao, Jiangjiang Liu +4
With the rapid advancement of Multimodal Large Language Models (MLLMs), a variety of benchmarks have been introduced to evaluate their capabilities. While most evaluations have foc…