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cs.CV2026

TRUST: Efficient Abdominal Trauma Recognition via Image-to-Ultrasound-Video Transfer Learning

Enguang Wang, Hao Zhou, Shuo Gao +2

Abdominal ultrasound is indispensable for rapid, noninvasive trauma triage. However, interpreting the subtle dynamic cues embedded in continuous scanning is time-intensive and oper…

cs.CV2026

Predictive Regularization Against Visual Representation Degradation in Multimodal Large Language Models

Enguang Wang, Qiang Wang, Yuanchen Wu +5

While Multimodal Large Language Models (MLLMs) excel at vision-language tasks, the cost of their language-driven training on internal visual foundational competence remains unclear…

cs.CV2025

Sharpness-aware Dynamic Anchor Selection for Generalized Category Discovery

Zhimao Peng, Enguang Wang, Fei Yang +2

Generalized category discovery (GCD) is an important and challenging task in open-world learning. Specifically, given some labeled data of known classes, GCD aims to cluster unlabe…

cs.CV2025

Predictive Sample Assignment for Semantically Coherent Out-of-Distribution Detection

Zhimao Peng, Enguang Wang, Xialei Liu +1

Semantically coherent out-of-distribution detection (SCOOD) is a recently proposed realistic OOD detection setting: given labeled in-distribution (ID) data and mixed in-distributio…

cs.CV2025

Restoring Forgotten Knowledge in Non-Exemplar Class Incremental Learning through Test-Time Semantic Evolution

Haori Lu, Xusheng Cao, Linlan Huang +3

Continual learning aims to accumulate knowledge over a data stream while mitigating catastrophic forgetting. In Non-exemplar Class Incremental Learning (NECIL), forgetting arises d…

cs.CV2025

Learning Part Knowledge to Facilitate Category Understanding for Fine-Grained Generalized Category Discovery

Enguang Wang, Zhimao Peng, Zhengyuan Xie +3

Generalized Category Discovery (GCD) aims to classify unlabeled data containing both seen and novel categories. Although existing methods perform well on generic datasets, they str…