10 papers
LiST: Local-Simplex Test-Time LoRA Fusion
Yihua Shao, Jia Li, Siyu Chen +12
Task-specific LoRA adapters offer a modular way to specialize large language and vision-language models. However, existing adapter composition methods are mostly static and cannot…
Medical SAM3: A Foundation Model for Universal Prompt-Driven Medical Image Segmentation
Chongcong Jiang, Tianxingjian Ding, Chuhan Song +7
Promptable segmentation foundation models such as SAM3 have demonstrated strong generalization capabilities through interactive and concept-based prompting. However, their direct a…
ICM-Fusion: In-Context Meta-Optimized LoRA Fusion for Multi-Task Adaptation
Yihua Shao, Xiaofeng Lin, Xinwei Long +7
Enabling multi-task adaptation in pre-trained Low-Rank Adaptation (LoRA) models is crucial for enhancing their generalization capabilities. Most existing pre-trained LoRA fusion me…
EventVAD: Training-Free Event-Aware Video Anomaly Detection
Yihua Shao, Haojin He, Sijie Li +11
Video Anomaly Detection~(VAD) focuses on identifying anomalies within videos. Supervised methods require an amount of in-domain training data and often struggle to generalize to un…
GM-MoE: Low-Light Enhancement with Gated-Mechanism Mixture-of-Experts
Minwen Liao, Hao Bo Dong, Xinyi Wang +3
Low-light enhancement has wide applications in autonomous driving, 3D reconstruction, remote sensing, surveillance, and so on, which can significantly improve information utilizati…
In-Context Meta LoRA Generation
Yihua Shao, Minxi Yan, Yang Liu +12
Low-rank Adaptation (LoRA) has demonstrated remarkable capabilities for task specific fine-tuning. However, in scenarios that involve multiple tasks, training a separate LoRA model…