Shrink and Expose: Locate Edge Coarse-to-Fine for Camouflaged Object Detection

Kejun Kang, Yixiu Liu, Yaoqi Sun, Shangdong Zhu, Fawei Ge, Wei Wang, Chenggang Yan, Zhigao Zheng

Abstract: Precisely locating the edges of objects camouflaged as background is an exceedingly complicated task. Existing camouflaged object detection (COD) methods can accurately pinpoint the positions of objects, but the edges remain ambiguous. To tackle this challenge, we propose a multi-stage refinement framework to locate the precise edges of camouflaged objects coarse-to-fine, coined SAE, which integrates “shrink” and “expose” in each stage. In this paper, we transform the process by which humans capture object edges into a multi-stage shrinking process. Firstly, we obtain the edge probability distribution by subtracting a negative prediction result from a positive prediction result, thus proposing the positive-negative edge shrink module. The edge probability distribution from the previous stage is then explicitly propagated to the current stage to enhance the edge texture details. Second, we introduce edge exposure attention to expose significant edges, which also reflects the impact of the previous stage’s edge state and edge probability distribution on the current stage’s edge state. Finally, to eliminate state transition and edge deformation anomalies, we propose the abnormal localization restriction to ensure the proper execution of “shrink” and “expose”. Experimental results show that our model significantly outperforms recent SOTA methods on three COD benchmarks.