Vision-language models (VLMs) often generate massive visual tokens that greatly increase inference latency and memory footprint; while training-free token pruning offers a practical remedy, existing methods still struggle to balance local evidence and global context under aggressive compression. We propose Focus-Scan-Refine (FSR), a human-inspired, plug-and-play pruning framework that mimics how humans answer visual questions: focus on key evidence, then scan globally if needed, and refine the scanned context by aggregating relevant details. FSR first focuses on key evidence by combining visual importance with instruction relevance, avoiding the bias toward visually salient but query-irrelevant regions. It then scans for complementary context conditioned on the focused set, selecting tokens that are most different from the focused evidence. Finally, FSR refines the scanned context by aggregating nearby informative tokens into the scan anchors via similarity-based assignment and score-weighted merging, without increasing the token budget. Extensive experiments across multiple VLM backbones and vision-language benchmarks show that FSR consistently improves the accuracy-efficiency trade-off over existing state-of-the-art pruning methods. The source codes can be found at https://github.com/ILOT-code/FSR.
HiPose: Hierarchical Binary Surface Encoding and Correspondence Pruning for RGB-D 6DoF Object Pose Estimation
Zhejiang University（浙江大学）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；RPTU Kaiserslautern-Landau（凯撒斯劳滕-兰道工业大学）；Technische Universität München（慕尼黑工业大学）；Google（谷歌公司）
CNNPruner: Pruning Convolutional Neural Networks with Visual Analytics
Computer Network Information Center, Chinese Academy of Sciences（中国科学院计算机网络信息中心）；University of Chinese Academy of Sciences（中国科学院大学）；Visa Research（Visa研究院）；The Ohio State University（俄亥俄州立大学）
PruNeRF: Segment-Centric Dataset Pruning via 3D Spatial Consistency
Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院（KAIST））；NAVER AI Lab（NAVER AI实验室）；AI Institute of Seoul National University（首尔国立大学AI研究所）；AITRICS
Self-pruning Graph Neural Network for Predicting Inflammatory Disease Activity in Multiple Sclerosis from Brain MR Images
University of Zurich（苏黎世大学）；Technical University of Munich（慕尼黑工业大学）；Klinikum rechts der Isar, Technical University of Munich（慕尼黑工业大学 rechts der Isar 医院）；Imperial College London（帝国理工学院）；The First Affiliated Hospital of Xi’an Jiaotong University（西安交通大学第一附属医院）
STAR-Pro: Stage-Wise Token Adaptive Reduction with Progressive Refinement for Efficient Large Vision-Language Models
Peking University（北京大学）；Nanyang Technological University, Singapore（南洋理工大学）；University of Electronic Science and Technology of China（电子科技大学）；University of Michigan, Ann Arbor（密歇根大学安娜堡分校）；De Artificial Intelligence Lab（De人工智能实验室）
Less is More: Token-Efficient Video-QA via Adaptive Frame-Pruning and Semantic Graph Integration
The Hong Kong University of Science and Technology (Guangzhou), China（香港科技大学（广州）中国）；The Hong Kong University of Science and Technology, Hong Kong SAR, China（香港科技大学，香港特别行政区，中国）
How Many Visual Tokens Do Multimodal Language Models Need? Scaling Visual Token Pruning with F^3A
School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China（东北大学计算机科学与工程学院，沈阳 110819，中国）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；School of Computer and Communication Engineering, Northeastern University, Qinhuangdao 066004, China（东北大学计算机与通信工程学院，秦皇岛 066004，中国）
Supporting Vision-Language Model Inference with Confounder-pruning Knowledge Prompt
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Software Chinese Academy of Sciences（中国科学院软件研究所）；Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
Drone Stereo Vision for Radiata Pine Branch Detection and Distance Measurement: Utilizing Deep Learning and YOLO Integration
Centre for Data Science and Artificial Intelligence（数据科学与人工智能中心）；Victoria University of Wellington（惠灵顿维多利亚大学）；Department of Computer Science and Software Engineering（计算机科学与软件工程系）；Canterbury University（坎特伯雷大学）
