Referring Camouflaged Object Detection

We consider the problem of referring camouflaged object detection (Ref-COD), a new task that aims to segment specified camouflaged objects based on a small set of referring images with salient target objects. We first assemble a large-scale dataset, called R2C7K, which consists of 7K images covering 64 object categories in real-world scenarios. Then, we develop a simple but strong dual-branch framework, dubbed R2CNet, with a reference branch embedding the common representations of target objects from referring images and a segmentation branch identifying and segmenting camouflaged objects under the guidance of the common representations. In particular, we design a Referring Mask Generation module to generate pixel-level prior mask and a Referring Feature Enrichment module to enhance the capability of identifying specified camouflaged objects. Extensive experiments show the superiority of our Ref-COD methods over their COD counterparts in segmenting specified camouflaged objects and identifying the main body of target objects. Our code and dataset are publicly available atthis https URL.

@article{zhang2025_2306.07532,
  title={ Referring Camouflaged Object Detection },
  author={ Xuying Zhang and Bowen Yin and Zheng Lin and Qibin Hou and Deng-Ping Fan and Ming-Ming Cheng },
  journal={arXiv preprint arXiv:2306.07532},
  year={ 2025 }
}