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<p>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 <i>S</i>emantic-spatial guided <i>C</i>ontext <i>P</i>ropagation <i>N</i>etwork (<i>SCPNet</i>) 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 <a href="https://github.com/RJC0608/SCPNet">https://github.com/RJC0608/SCPNet</a>.</p>

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Semantic-spatial guided context propagation network for camouflaged object detection

  • 期刊论文

  • 发表日期：

    2025年1月20日

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

摘要

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.