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cs.CV2025
Commonality in Few: Few-Shot Multimodal Anomaly Detection via Hypergraph-Enhanced Memory
Yuxuan Lin, Hanjing Yan, Xuan Tong +6
Few-shot multimodal industrial anomaly detection is a critical yet underexplored task, offering the ability to quickly adapt to complex industrial scenarios. In few-shot settings,…
cs.CV2025
Component-aware Unsupervised Logical Anomaly Generation for Industrial Anomaly Detection
Xuan Tong, Yang Chang, Qing Zhao +9
Anomaly detection is critical in industrial manufacturing for ensuring product quality and improving efficiency in automated processes. The scarcity of anomalous samples limits tra…
cs.CV2025
A Holistically Point-guided Text Framework for Weakly-Supervised Camouflaged Object Detection
Tsui Qin Mok, Shuyong Gao, Haozhe Xing +3
Weakly-Supervised Camouflaged Object Detection (WSCOD) has gained popularity for its promise to train models with weak labels to segment objects that visually blend into their surr…