mlsanityData to 14 Oct 2026

Exploring Depth Contribution for Camouflaged Object Detection

arXiv 2106.13217 ↗First posted 24 Jun 2021cs.CVcited by 14

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Abstract (arXiv)

Camouflaged object detection (COD) aims to segment camouflaged objects hiding in the environment, which is challenging due to the similar appearance of camouflaged objects and their surroundings. Research in biology suggests depth can provide useful object localization cues for camouflaged object discovery. In this paper, we study the depth contribution for camouflaged object detection, where the depth maps are generated with existing monocular depth estimation (MDE) methods. Due to the domain gap between the MDE dataset and our COD dataset, the generated depth maps are not accurate enough to be directly used. We then introduce two solutions to avoid the noisy depth maps from dominating the training process. Firstly, we present an auxiliary depth estimation branch ("ADE"), aiming to regress the depth maps. We find that "ADE" is especially necessary for our "generated depth" scenario. Secondly, we introduce a multi-modal confidence-aware loss function via a generative adversarial network to weigh the contribution of depth for camouflaged object detection. Our extensive experiments on various camouflaged object detection datasets explain that the existing "sensor depth" based RGB-D segmentation techniques work poorly with "generated depth", and our proposed two solutions work cooperatively, achieving effective depth contribution exploration for camouflaged object detection.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 35 of its 78 references that are in the map. First posted on arXiv 24 Jun 2021.

Builds on

35 of 78 references.

References · 35

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23 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 6; 2024: 2; 2025: 3; 2026: 2.

14 papers.

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  1. 2023Strategic Preys Make Acute Predators: Enhancing Camouflaged Object Detectors by Generating Camouflaged Objectscited by 56
  2. 2022Source-free Depth for Object Pop-outcited by 52
  3. 2023Advances in Deep Concealed Scene Understandingcited by 47
  4. 2024Exploring Deeper! Segment Anything Model with Depth Perception for Camouflaged Object Detectioncited by 20
  5. 2023Referring Camouflaged Object Detectioncited by 19
  6. 2023Open-Vocabulary Camouflaged Object Segmentationcited by 14
  7. 2025Integrating Extra Modality Helps Segmentor Find Camouflaged Objects Wellcited by 14
  8. 2023Object Segmentation by Mining Cross-Modal Semanticscited by 12
  9. 2025UCOD-DPL: Unsupervised Camouflaged Object Detection via Dynamic Pseudo-label Learningcited by 10
  10. 2023Large Model Based Referring Camouflaged Object Detectioncited by 8
  11. 2024Depth Awakens: A Depth-perceptual Attention Fusion Network for RGB-D Camouflaged Object Detectioncited by 8
  12. 2025Deep Learning in Concealed Dense Predictioncited by 6
2 more
  1. 2026Language-Guided Structure-Aware Network for Camouflaged Object Detectioncited by 0
  2. 2026BED-SAM2: Boundary-Enhanced-Depth SAM2 via Monocular Geometric Priorscited by 0