9 citations · 9 across the 5 of their papers we have counts for
4 papers · 1 filter
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.…
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…
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…
Rethinking the Open-Loop Evaluation of End-to-End Autonomous Driving in nuScenes
Jiang-Tian Zhai, Ze Feng, Jinhao Du +6
Modern autonomous driving systems are typically divided into three main tasks: perception, prediction, and planning. The planning task involves predicting the trajectory of the ego…