arXiv Machine Learning
Sep 4

Who Speaks for the Pruned? Visual Token Pruning as Coverage Optimization

CoverPruner is a training‑free visual token pruner that reframes token pruning as a representational coverage maximization problem. Instead of selecting which tokens to keep, it asks which surviving token best represents each removed token for a vision‑language model. Experiments on various VLM architectures show that CoverPruner consistently outperforms existing methods, especially under high compression rates.

By Qingchan Zhu, Weihang You, Hanqi Jiang, Changdi Yang, Tianming Liu, Geng Yuan
arXiv Computer Vision
3d ago

MiCo: Mutual Information Coverage Optimization through Semantic Erasure Modeling for Efficient MLLM Inference

arXiv:2609.34330v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) have demonstrated impressive performance in multimodal understanding, but processing large numbers of visu...

By Tinghao Wang, Yichen Guo, Qizhe Zhang, Yuan Zhang, Weimin Ouyang, Rui Huang, Jiajun Cao, Sixiang Chen, Hao Jiang, Jixian Wu, Zheng Lu, Bofan Zhu, Renyuan Li, Shanghang Zhang