VisionTrim: Unified Vision Token Compression for Training-Free MLLM Acceleration
Multimodal large language models (MLLMs) suffer from high computational costs due to excessive visual tokens, particularly in high-resolution and video-based scenarios. Existing token reduction methods typically focus on isolated pipeline components and often neglect textual alignment, leading to performance degradation. In this paper, we propose VisionTrim, a unified framework for training-free MLLM acceleration, integrating two effective plug-and-play modules: 1) the Dominant Vision Token Selection (DVTS) module, which preserves essential visual tokens via a global-local view, and 2) the Text-Guided Vision Complement (TGVC) module, which facilitates context-aware token merging guided by textual cues. Extensive experiments across diverse image and video multimodal benchmarks demonstrate the performance superiority of our VisionTrim, advancing practical MLLM deployment in real-world applications. The code is available at: https://github.com/hanxunyu/VisionTrim.
69803d8d6676f9332270666c
Hanxun Yu
claimed_verified
2026-02-02T16:52:23.428Z
652d3023cdb2a91205709b6a
https://cdn-avatars.huggingface.co/v1/production/uploads/652d3023cdb2a91205709b6a/x-MaRkBwSdZ0xIYDJg09u.jpeg
Hanxun Yu
JonnyYu828
user
JonnyYu828
69803d8d6676f9332270666d
Wentong Li
69803d8d6676f9332270666e
Xuan Qu
69803d8d6676f9332270666f
Song Wang
69803d8d6676f93322706670
Junbo Chen
69803d8d6676f93322706671
Jianke Zhu
VisionTrim: Unified Vision Token Compression for Training-Free MLLM Acceleration
Multimodal large language models (MLLMs) suffer from high computational costs due to excessive visual tokens, particularly in high-resolution and video-based scenarios. Existing token reduction methods typically focus on isolated pipeline components and often neglect textual alignment, leading to performance degradation. In this paper, we propose VisionTrim, a unified framework for training-free MLLM acceleration, integrating two effective plug-and-play modules: 1) the Dominant Vision Token Selection (DVTS) module, which preserves essential visual tokens via a global-local view, and 2) the Text-Guided Vision Complement (TGVC) module, which facilitates context-aware token merging guided by textual cues. Extensive experiments across diverse image and video multimodal benchmarks demonstrate the performance superiority of our VisionTrim, advancing practical MLLM deployment in real-world applications. The code is available at: https://github.com/hanxunyu/VisionTrim.
VisionTrim is a training-free framework that accelerates multimodal large language models by selecting dominant visual tokens and merging them with text-guided complementation, improving efficiency without performance loss.
Multimodal large language models visual tokens token reduction Dominant Vision Token Selection Text-Guided Vision Complement training-free multimodal benchmarks