Integrating Part-Object Relationship and Contrast for Camouflaged Object Detection

Yi Liu, Dingwen Zhang, Qiang Zhang*, Jungong Han

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Citations (SciVal)
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Object detectors that solely rely on image contrast are struggling to detect camouflaged objects in images because of the high similarity between camouflaged objects and their surroundings. To address this issue, in this paper, we investigate the role of the part-object relationship for camouflaged object detection. Specifically, we propose a Part-Object relationship and Contrast Integrated Network (POCINet) covering both search and identification stages, where each stage adopts an appropriate scheme to engage the contrast information and part-object relational knowledge for camouflaged pattern decoding. Besides, we bridge these two stages via a Search-to-Identification Guidance (SIG) module, in which the search result, as well as decoded semantic knowledge, jointly enhances the features encoding ability of the identification stage. Experimental results demonstrate the superiority of our algorithm on three datasets. Notably, our algorithm raises Fβ of the best existing method by approximately 17 points on the CPD1K dataset.
Original languageEnglish
Pages (from-to)5154-5166
Number of pages13
JournalIEEE Transactions on Information Forensics and Security
Early online date04 Nov 2021
Publication statusPublished - 11 Nov 2021


  • Camouflaged object detection
  • contrast
  • encoder-decoder
  • multi-stage
  • part-object relationships


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