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cs.CV2026
Not All Visual Tokens Are Equally Safe to Remove:Consequence-Sensitive Visual Token Compression
Jingbo Wen, Liang He, Mingyu Cao +4
Visual token compression for vision--language models (VLMs) has largely relied on criteria such as attention, redundancy, and uncertainty to maximize average accuracy under a fixed…
cs.CV2026
TSegAgent: Zero-Shot Tooth Segmentation via Geometry-Aware Vision-Language Agents
Shaojie Zhuang, Lu Yin, Guangshun Wei +3
Automatic tooth segmentation and identification from intra-oral scanned 3D models are fundamental problems in digital dentistry, yet most existing approaches rely on task-specific…
cs.CV2026
Self-Supervised Federated Learning under Data Heterogeneity for Label-Scarce Diatom Classification
Mingkun Tan, Xilu Wang, Michael Kloster +1
Label-scarce visual classification under decentralized and heterogeneous data is a fundamental challenge in pattern recognition, especially when sites exhibit partially overlapping…