Camouflaged Object Detection

Camouflaged object detection (COD) focuses on automatically identifying objects that blend seamlessly into their backgrounds, a challenging computer vision problem with applications in diverse fields like surveillance and wildlife monitoring. Current research emphasizes improving feature representation through techniques like frequency domain analysis and multi-scale feature fusion, often employing advanced architectures such as vision transformers and diffusion models, as well as adapting pre-trained models like Segment Anything Model (SAM). The development of more robust and efficient COD methods is crucial for advancing various applications, particularly those requiring accurate object segmentation in complex visual scenes. Furthermore, research is exploring weakly-supervised and even self-supervised approaches to reduce the reliance on extensive, labor-intensive pixel-level annotations.