mlsanityData to 7 Oct 2026

Referring Camouflaged Object Detection

arXiv 2306.07532 ↗First posted 13 Jun 2023cs.CVcited by 19

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Abstract (arXiv)

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 at https://github.com/zhangxuying1004/RefCOD.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 66 of its 112 references that are in the map. First posted on arXiv 13 Jun 2023.

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66 of 112 references.

References · 66

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  14. 2020Multi-task Collaborative Network for Joint Referring Expression Comprehension and Segmentationcited by 204
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 4; 2025: 7; 2026: 4.

19 papers.

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  1. 2023DFormer: Rethinking RGBD Representation Learning for Semantic Segmentationcited by 58
  2. 2024A Survey of Camouflaged Object Detection and Beyondcited by 37
  3. 2023YOLO-MS: Rethinking Multi-Scale Representation Learning for Real-time Object Detectioncited by 33
  4. 2023TeMO: Towards Text-Driven 3D Stylization for Multi-Object Meshescited by 14
  5. 2024When SAM2 Meets Video Camouflaged Object Segmentation: A Comprehensive Evaluation and Adaptationcited by 11
  6. 2025MLLM-Selector: Necessity and Diversity-driven High-Value Data Selection for Enhanced Visual Instruction Tuningcited by 9
  7. 2023Large Model Based Referring Camouflaged Object Detectioncited by 8
  8. 2025Deep Learning in Concealed Dense Predictioncited by 6
  9. 2026Refining Context-Entangled Content Segmentation via Curriculum Selection and Anti-Curriculum Promotioncited by 5
  10. 2024Adaptive Guidance Learning for Camouflaged Object Detectioncited by 4
  11. 2025Segment Concealed Objects with Incomplete Supervisioncited by 4
  12. 2025Marine Saliency Segmenter: Object-Focused Conditional Diffusion with Region-Level Semantic Knowledge Distillationcited by 2
7 more
  1. 2024Discriminative Consensus Mining with A Thousand Groups for More Accurate Co-Salient Object Detectioncited by 0
  2. 2025RefAtomNet++: Advancing Referring Atomic Video Action Recognition using Semantic Retrieval based Multi-Trajectory Mambacited by 0
  3. 2025MSRNet: A Multi-Scale Recursive Network for Camouflaged Object Detectioncited by 0
  4. 2025RefOnce: Distilling References into a Prototype Memory for Referring Camouflaged Object Detectioncited by 0
  5. 2026UAV-CB: A Complex-Background RGB-T Dataset and Local Frequency Bridge Network for UAV Detectioncited by 0
  6. 2026EviRCOD: Evidence-Guided Probabilistic Decoding for Referring Camouflaged Object Detectioncited by 0
  7. 2026Consensus-Aware Multi-Source Fusion for Reference-Guided Camouflaged Object Detectioncited by 0