Semantic-spatial guided context propagation network for camouflaged object detection

Junchao Ren, Qiao Zhang, Bingbing Kang, Yuxi Zhong, Min He, Yanliang Ge, Hongbo Bi

Abstract: Camouflaged object detection (COD) aims to detect objects that blend in with their surroundings and is a challenging task in computer vision. High-level semantic information and low-level spatial information play important roles in localizing camouflaged objects and reinforcing spatial cues. However, current COD methods directly connect high-level features with low-level features, ignoring the importance of the respective features. In this paper, we design a Semantic-spatial guided Context Propagation Network (SCPNet) to efficiently mine semantic and spatial features while enhancing their feature representations. Firstly, we design a twin positioning module (TPM) to explore semantic cues to accurately locate camouflaged objects. Afterward, we introduce a spatial awareness module (SAM) to mine spatial cues in shallow features deeply. Finally, we develop a context propagation module (CPM) to assign semantic and spatial cues to multi-level features and enhance their feature representations. Experimental results show that our SCPNet outperforms state-of-the-art methods on three challenging datasets. Codes will be made available at https://github.com/RJC0608/SCPNet.