AI RESEARCH
FCL-COD: Weakly Supervised Camouflaged Object Detection with Frequency-aware and Contrastive Learning
arXiv CS.CV
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ArXi:2603.22969v1 Announce Type: new Existing camouflage object detection (COD) methods typically rely on fully-supervised learning guided by mask annotations. However, obtaining mask annotations is time-consuming and labor-intensive. Compared to fully-supervised methods, existing weakly-supervised COD methods exhibit significantly poorer performance. Even for the Segment Anything Model (SAM), there are still challenges in handling weakly-supervised camouflage object detection (WSCOD), such as: a. non-camouflage target responses, b. local responses, c. extreme responses, and d.