mlsanityData to 7 Oct 2026

Camouflaged Instance Segmentation In-The-Wild: Dataset, Method, and Benchmark Suite

arXiv 2103.17123 ↗First posted 31 Mar 2021cs.CVcited by 16

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Abstract (arXiv)

This paper pushes the envelope on decomposing camouflaged regions in an image into meaningful components, namely, camouflaged instances. To promote the new task of camouflaged instance segmentation of in-the-wild images, we introduce a dataset, dubbed CAMO++, that extends our preliminary CAMO dataset (camouflaged object segmentation) in terms of quantity and diversity. The new dataset substantially increases the number of images with hierarchical pixel-wise ground truths. We also provide a benchmark suite for the task of camouflaged instance segmentation. In particular, we present an extensive evaluation of state-of-the-art instance segmentation methods on our newly constructed CAMO++ dataset in various scenarios. We also present a camouflage fusion learning (CFL) framework for camouflaged instance segmentation to further improve the performance of state-of-the-art methods. The dataset, model, evaluation suite, and benchmark will be made publicly available on our project page: https://sites.google.com/view/ltnghia/research/camo_plus_plus

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 34 of its 57 references that are in the map. First posted on arXiv 31 Mar 2021.

Builds on

34 of 57 references.

References · 34

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  3. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  4. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  5. 2017Mask R-CNNcited by 7,259
  6. 2016Aggregated Residual Transformations for Deep Neural Networkscited by 2,540
  7. 2019FCOS: Fully Convolutional One-Stage Object Detectioncited by 1,573
  8. 2017Cascade R-CNN: Delving into High Quality Object Detectioncited by 1,275
  9. 2019LVIS: A Dataset for Large Vocabulary Instance Segmentationcited by 1,215
  10. 2019Objects as Pointscited by 1,172
  11. 2018Path Aggregation Network for Instance Segmentationcited by 796
  12. 2019YOLACT: Real-time Instance Segmentationcited by 441
22 more
  1. 2016R-FCN: Object Detection via Region-based Fully Convolutional Networkscited by 414
  2. 2020Conditional Convolutions for Instance Segmentationcited by 314
  3. 2019SOLO: Segmenting Objects by Locationscited by 289
  4. 2019Mask Scoring R-CNNcited by 265
  5. 2019CenterMask : Real-Time Anchor-Free Instance Segmentationcited by 200
  6. 2019PolarMask: Single Shot Instance Segmentation with Polar Representationcited by 188
  7. 2020BlendMask: Top-Down Meets Bottom-Up for Instance Segmentationcited by 144
  8. 2019Multi-task Learning For Detecting and Segmenting Manipulated Facial Images and Videoscited by 130
  9. 2016Fully Convolutional Instance-aware Semantic Segmentationcited by 122
  10. 2015Instance-aware Semantic Segmentation via Multi-task Network Cascadescited by 120
  11. 2019TensorMask: A Foundation for Dense Object Segmentationcited by 101
  12. 2016It's Moving! A Probabilistic Model for Causal Motion Segmentation in Moving Camera Videoscited by 71
  13. 2017On Loss Functions for Deep Neural Networks in Classificationcited by 68
  14. 2019AdaptIS: Adaptive Instance Selection Networkcited by 64
  15. 2020Betrayed by Motion: Camouflaged Object Discovery via Motion Segmentationcited by 62
  16. 2016InstanceCut: from Edges to Instances with MultiCutcited by 48
  17. 2015Instance-Level Segmentation for Autonomous Driving with Deep Densely Connected MRFscited by 32
  18. 2019RetinaMask: Learning to predict masks improves state-of-the-art single-shot detection for freecited by 24
  19. 2019EmbedMask: Embedding Coupling for One-stage Instance Segmentationcited by 15
  20. 2019Polyp Detection and Segmentation using Mask R-CNN: Does a Deeper Feature Extractor CNN Always Perform Better?cited by 13
  21. 2016Joint Graph Decomposition and Node Labeling: Problem, Algorithms, Applicationscited by 4
  22. 2018Superpixel-enhanced Pairwise Conditional Random Field for Semantic Segmentationcited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 3; 2023: 5; 2024: 2; 2025: 2; 2026: 3.

16 papers.

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  1. 2022High-Quality Entity Segmentationcited by 93
  2. 2023Advances in Deep Concealed Scene Understandingcited by 47
  3. 2022Feature Aggregation and Propagation Network for Camouflaged Object Detectioncited by 40
  4. 2024A Survey of Camouflaged Object Detection and Beyondcited by 37
  5. 2022OSFormer: One-Stage Camouflaged Instance Segmentation with Transformerscited by 30
  6. 2023MaskDiff: Modeling Mask Distribution with Diffusion Probabilistic Model for Few-Shot Instance Segmentationcited by 18
  7. 2024High-Quality Entity Segmentation and Groundingcited by 13
  8. 2023A Unified Query-based Paradigm for Camouflaged Instance Segmentationcited by 7
  9. 2025Deep Learning in Concealed Dense Predictioncited by 6
  10. 2023The Art of Camouflage: Few-Shot Learning for Animal Detection and Segmentationcited by 5
  11. 2023CamoFA: A Learnable Fourier-based Augmentation for Camouflage Segmentationcited by 2
  12. 2025Expose Camouflage in the Water: Underwater Camouflaged Instance Segmentation and Datasetcited by 2
4 more
  1. 2026Segment Anything with Motion, Geometry, and Semantic Adaptation for Complex Nonlinear Visual Object Trackingcited by 2
  2. 2026HyperCOD: The First Challenging Benchmark and Baseline for Hyperspectral Camouflaged Object Detectioncited by 1
  3. 2026CAMotion: A High-Quality Benchmark for Camouflaged Moving Object Detection in the Wildcited by 1
  4. 2021GUNNEL: Guided Mixup Augmentation and Multi-Model Fusion for Aquatic Animal Segmentationcited by 0