10 papers
Reasoning with Memory: A Temporal Granularity-Adaptive Framework for Training-Free Long Video Understanding
Linghao Meng, Qiankun Li, Junyuan Mao +7
While Multimodal Large Language Models (MLLMs) demonstrate superior generalization in fundamental video tasks, restricted context windows limit their long video understanding. To a…
Dual-Adaptive SAM3: Hierarchical Routing over Low-Rank Expert Layers for Parameter-Efficient Medical Image Segmentation
Ying Chen, Jinyue Li, Kun Wang +2
The Segment Anything Model with Concepts (SAM3) heralds a new paradigm for open-vocabulary segmentation through natural language interaction, offering significant potential for med…
PathMem: Toward Cognition-Aligned Memory Transformation for Pathology MLLMs
Jinyue Li, Yuci Liang, Qiankun Li +7
Computational pathology demands both visual pattern recognition and dynamic integration of structured domain knowledge, including taxonomy, grading criteria, and clinical evidence.…
NeuroPath: Practically Adopting Motor Imagery Decoding through EEG Signals
Jiani Cao, Kun Wang, Yang Liu +1
Motor Imagery (MI) is an emerging Brain-Computer Interface (BCI) paradigm where a person imagines body movements without physical action. By decoding scalp-recorded electroencephal…
Granulon: Awakening Pixel-Level Visual Encoders with Adaptive Multi-Granularity Semantics for MLLM
Junyuan Mao, Qiankun Li, Linghao Meng +5
Recent advances in multimodal large language models largely rely on CLIP-based visual encoders, which emphasize global semantic alignment but struggle with fine-grained visual unde…
SaFeR-ToolKit: Structured Reasoning via Virtual Tool Calling for Multimodal Safety
Zixuan Xu, Tiancheng He, Huahui Yi +7
Vision-language models remain susceptible to multimodal jailbreaks and over-refusal because safety hinges on both visual evidence and user intent, while many alignment pipelines su…