Rethinking Camouflaged Object Detection via Foreground-Background Interactive Learning

Chenxi Zhang, Qing Zhang, Jiayun Wu

Abstract: Camouflaged object detection focuses on the challenge of segmenting objects that visually blend into their background. The effectiveness of camouflage strategies hinges on how well objects interact with their background to minimize their visibility. Based on this insight, we propose a novel ForegroundBackground Interactive Learning Network (FBINet), which independently decouples foreground and background information, and facilitates bi-directional interactions. This allows the network to progressively complement each other, leading to high-quality predictions with clear boundaries. To the best of our knowledge, this is the first attempt to tackle the camouflaged object detection task through interactive learning between foreground and background, which not only better reveals camouflage patterns but also offers a new perspective in this field. Experimental results on three datasets demonstrate that our proposed FBINet outperforms current state-of-the-art methods in performance while maintaining a low computational cost, making it applicable for real-world scenarios. The code will be available at https://github.com/bbdjj/FBINet.