5 papers indexed
Chang Nie, Jiaju Wei, Junlan Feng, Chaoyou Fu, Caifeng Shan
Agentic video understanding equips models with long-term memory to autonomously process and respond to continuous, long-horizon multimodal streams. However, advanced video agents often rely on ``detective-style'' iterative reasoning for action control (e.g., $\mathtt{search}$) an…
Runzhi Deng, Yundi Hu, Yiming Zhong, Zhao Wang, Xixi Liu, Hongsong Wang, et al.
Large Multimodal Models (LMMs) show strong few-shot generalization, but industrial anomaly detection remains difficult because defects are small, input resolution is limited, and textual standards are not always grounded in visual evidence. Recent optimization-based methods impro…
Wen Dong, Zhao Wang, Shuangqing Zhang, Kai Sun, Ben Li, Guo-Sen Xie, et al.
Multimodal Large Language Models (MLLMs) excel in diverse vision tasks, but full-parameter retraining is computationally expensive as real-world knowledge evolves. Existing continual learning methods often suffer from semantic entanglement in parameter spaces across tasks, impedi…
Songping Wang, Yueming Lyu, Shiqi Liu, Chen Zhao, Ziyuan Chen, Ning Li, et al.
The rise of customized diffusion models has fueled a boom in personalized visual content creation, but it also introduces serious risks of malicious misuse, thereby posing threats to personal privacy. Image aesthetics are strongly correlated with human perception of image quality…
Xiao Song, Haonan Qin, Zhaoxu Zhang, Jiong Zhang, Yuqi Fang, Caifeng Shan
Large vision-language models (LVLMs) are increasingly used for clinical image understanding, yet they remain vulnerable to \emph{hallucinations}--producing textual findings or attributes not supported by the image. We present a vision-traceable hallucination detection framework t…