Video large language models have demonstrated remarkable capabilities in video understanding tasks. However, the redundancy of video tokens introduces significant computational overhead during inference, limiting their practical deployment. Many compression algorithms are proposed to prioritize retaining features with the highest attention scores to minimize perturbations in attention computations. However, the correlation between attention scores and their actual contribution to correct answers remains ambiguous. To address the above limitation, we propose a novel \textbf{C}ontribution-\textbf{a}ware token \textbf{Co}mpression algorithm for \textbf{VID}eo understanding (\textbf{CaCoVID}) that explicitly optimizes the token selection policy based on the contribution of tokens to correct predictions. First, we introduce a reinforcement learning-based framework that optimizes a policy network to select video token combinations with the greatest contribution to correct predictions. This paradigm shifts the focus from passive token preservation to active discovery of optimal compressed token combinations. Secondly, we propose a combinatorial policy optimization algorithm with online combination space sampling, which dramatically reduces the exploration space for video token combinations and accelerates the convergence speed of policy optimization. Extensive experiments on diverse video understanding benchmarks demonstrate the effectiveness of CaCoVID. Codes are available at https://github.com/LivingFutureLab/CaCoVID.
Less Is More, but Where? Dynamic Token Compression via LLM-Guided Keyframe Prior
Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；Shenzhen Loop Area Institute（深圳河套学院）；University of Chinese Academy of Sciences（中国科学院大学）；The Hong Kong University of Science and Technology（香港科技大学）
AdaTok: Adaptive Token Compression with Object-Aware Representations for Efficient Multimodal LLMs
Institute of Medical Technology, Peking University Health Science Center（北京大学医学部医学技术研究所）；Department of Biomedical Engineering, Peking University（北京大学生物医学工程系）；National Biomedical Imaging Center, Peking University（北京大学国家生物医学成像中心）
On the Adversarial Robustness of Large Vision-Language Models under Visual Token Compression
The Hong Kong Polytechnic University, Hong Kong（香港理工大学）；Nanyang Technological University, Singapore（南洋理工大学）；Chongqing University, Chongqing, China（重庆大学）；Research Centre for Privacy and Security Technologies in Future Smart Systems, PolyU（未来智能系统中的隐私与安全技术研究中心）
ForestPrune: High-ratio Visual Token Compression for Video Multimodal Large Language Models via Spatial-Temporal Forest Modeling
Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University（厦门大学多媒体可信感知与高效计算教育部重点实验室）；National University of Defense Technology（国防科技大学）
Fre-Res: Frequency-Residual Video Token Compression for Efficient Video MLLMs
College of Computer Science, National University of Defense Technology（计算机科学学院，国防科技大学）；Shien-Ming Wu School of Intelligent Engineering, South China University of Technology（智能工程谢民明伍学院，华南理工大学）
UHR-BAT: Budget-Aware Token Compression Vision-Language model for Ultra-High-Resolution Remote Sensing
School of Artificial Intelligence Science and Technology, Nanjing University（南京大学人工智能科学与技术学院）；School of Electronic Science and Engineering, Nanjing University（南京大学电子科学与工程学院）；School of Physics, Nanjing University（南京大学物理学院）
Bottlenecked semantic compression by representation reconstruction
Scientific Computing and Imaging Institute（科学计算与成像研究所）；University of Utah（犹他大学）；Princeton AI Lab（普林斯顿人工智能实验室）；Princeton University（普林斯顿大学）；National Institutes of Health（美国国立卫生研究院）
College of Computer Science and Technology, National University of Defense Technology（国防科技大学计算机科学与技术学院）；School of Computer Science, Beijing University of Posts and Telecommunications（北京邮电大学计算机学院）
Not All Visual Tokens Are Equally Safe to Remove:Consequence-Sensitive Visual Token Compression
The University of Sydney（悉尼大学）；Tongji University（同济大学）；University of Surrey（萨里大学）；Nankai University（南开大学）；Cornell University（康奈尔大学）；City University of Hong Kong（香港城市大学）
PVC: Progressive Visual Token Compression for Unified Image and Video Processing in Large Vision-Language Models
Tsinghua University（清华大学）；OpenGVLab（开放通用实验室）；Shanghai AI Laboratory（上海人工智能实验室）；SenseTime Research（商汤科技研究院）；Nanjing University（南京大学）；The Chinese University of Hong Kong（香港中文大学）
