mlsanityData to 7 Oct 2026

Structure-measure: A New Way to Evaluate Foreground Maps

arXiv 1708.00786 ↗First posted 2 Aug 2017cs.CVcited by 480

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Abstract (arXiv)

Foreground map evaluation is crucial for gauging the progress of object segmentation algorithms, in particular in the filed of salient object detection where the purpose is to accurately detect and segment the most salient object in a scene. Several widely-used measures such as Area Under the Curve (AUC), Average Precision (AP) and the recently proposed Fbw have been utilized to evaluate the similarity between a non-binary saliency map (SM) and a ground-truth (GT) map. These measures are based on pixel-wise errors and often ignore the structural similarities. Behavioral vision studies, however, have shown that the human visual system is highly sensitive to structures in scenes. Here, we propose a novel, efficient, and easy to calculate measure known an structural similarity measure (Structure-measure) to evaluate non-binary foreground maps. Our new measure simultaneously evaluates region-aware and object-aware structural similarity between a SM and a GT map. We demonstrate superiority of our measure over existing ones using 5 meta-measures on 5 benchmark datasets.

Key facts. Cited by 480 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Aug 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 77; 2022: 80; 2023: 88; 2024: 90; 2025: 83; 2026: 62.

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  1. 2022Kubric: A scalable dataset generatorcited by 373
  2. 2021Concealed Object Detectioncited by 212
  3. 2021Simultaneously Localize, Segment and Rank the Camouflaged Objectscited by 198
  4. 2021Visual Saliency Transformercited by 175
  5. 2021Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformerscited by 159
  6. 2021Camouflaged Object Segmentation with Distraction Miningcited by 151
  7. 2022Zoom In and Out: A Mixed-scale Triplet Network for Camouflaged Object Detectioncited by 145
  8. 2021Salient Object Detection via Integrity Learningcited by 133
  9. 2021Uncertainty-aware Joint Salient Object and Camouflaged Object Detectioncited by 114
  10. 2024Bilateral Reference for High-Resolution Dichotomous Image Segmentationcited by 112
  11. 2022Highly Accurate Dichotomous Image Segmentationcited by 110
  12. 2021Mutual Graph Learning for Camouflaged Object Detectioncited by 103
38 more
  1. 2023Can SAM Segment Anything? When SAM Meets Camouflaged Object Detectioncited by 99
  2. 2023Weakly-Supervised Concealed Object Segmentation with SAM-based Pseudo Labeling and Multi-scale Feature Groupingcited by 92
  3. 2023Feature Shrinkage Pyramid for Camouflaged Object Detection with Transformerscited by 87
  4. 2021Context-aware Cross-level Fusion Network for Camouflaged Object Detectioncited by 86
  5. 2022Video Polyp Segmentation: A Deep Learning Perspectivecited by 81
  6. 2021Full-Duplex Strategy for Video Object Segmentationcited by 78
  7. 2022High-resolution Iterative Feedback Network for Camouflaged Object Detectioncited by 78
  8. 2021Automatic Polyp Segmentation via Multi-scale Subtraction Networkcited by 77
  9. 2021Progressively Normalized Self-Attention Network for Video Polyp Segmentationcited by 75
  10. 2023SAM Struggles in Concealed Scenes -- Empirical Study on Segment Anythingcited by 75
  11. 2021Specificity-preserving RGB-D Saliency Detectioncited by 70
  12. 2023AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt Encodercited by 70
  13. 2022Boundary-Guided Camouflaged Object Detectioncited by 68
  14. 2021Kaleido-BERT: Vision-Language Pre-training on Fashion Domaincited by 67
  15. 2022Deep Gradient Learning for Efficient Camouflaged Object Detectioncited by 65
  16. 2022Pyramid Grafting Network for One-Stage High Resolution Saliency Detectioncited by 63
  17. 2024SAM2-UNet: Segment Anything 2 Makes Strong Encoder for Natural and Medical Image Segmentationcited by 63
  18. 2022Implicit Motion Handling for Video Camouflaged Object Detectioncited by 60
  19. 2022SwinNet: Swin Transformer drives edge-aware RGB-D and RGB-T salient object detectioncited by 59
  20. 2023Can SAM Segment Polyps?cited by 59
  21. 2022CamoFormer: Masked Separable Attention for Camouflaged Object Detectioncited by 58
  22. 2023DFormer: Rethinking RGBD Representation Learning for Semantic Segmentationcited by 58
  23. 2021Learning Generative Vision Transformer with Energy-Based Latent Space for Saliency Predictioncited by 57
  24. 2021Deep RGB-D Saliency Detection with Depth-Sensitive Attention and Automatic Multi-Modal Fusioncited by 56
  25. 2023Strategic Preys Make Acute Predators: Enhancing Camouflaged Object Detectors by Generating Camouflaged Objectscited by 56
  26. 2022A Unified Transformer Framework for Group-based Segmentation: Co-Segmentation, Co-Saliency Detection and Video Salient Object Detectioncited by 51
  27. 2024Vivim: a Video Vision Mamba for Medical Video Segmentationcited by 51
  28. 2021RGB-D Saliency Detection via Cascaded Mutual Information Minimizationcited by 50
  29. 2023ZoomNeXt: A Unified Collaborative Pyramid Network for Camouflaged Object Detectioncited by 48
  30. 2021CAVER: Cross-Modal View-Mixed Transformer for Bi-Modal Salient Object Detectioncited by 47
  31. 2023Advances in Deep Concealed Scene Understandingcited by 47
  32. 2021TriTransNet: RGB-D Salient Object Detection with a Triplet Transformer Embedding Networkcited by 45
  33. 2022Weakly-Supervised Camouflaged Object Detection with Scribble Annotationscited by 43
  34. 2023CaraNet: Context Axial Reverse Attention Network for Segmentation of Small Medical Objectscited by 43
  35. 2025Seg-R1: Segmentation Can Be Surprisingly Simple with Reinforcement Learningcited by 43
  36. 2021Fast Camouflaged Object Detection via Edge-based Reversible Re-calibration Networkcited by 42
  37. 2021Boundary-Aware Segmentation Network for Mobile and Web Applicationscited by 41
  38. 2022Feature Aggregation and Propagation Network for Camouflaged Object Detectioncited by 40

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