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Image Segmentation Using Deep Learning: A Survey

arXiv 2001.05566 ↗First posted 15 Jan 2020cs.CV cs.LGcited by 659

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Abstract (arXiv)

Image segmentation is a key topic in image processing and computer vision with applications such as scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, and image compression, among many others. Various algorithms for image segmentation have been developed in the literature. Recently, due to the success of deep learning models in a wide range of vision applications, there has been a substantial amount of works aimed at developing image segmentation approaches using deep learning models. In this survey, we provide a comprehensive review of the literature at the time of this writing, covering a broad spectrum of pioneering works for semantic and instance-level segmentation, including fully convolutional pixel-labeling networks, encoder-decoder architectures, multi-scale and pyramid based approaches, recurrent networks, visual attention models, and generative models in adversarial settings. We investigate the similarity, strengths and challenges of these deep learning models, examine the most widely used datasets, report performances, and discuss promising future research directions in this area.

Key facts. Cited by 659 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Jan 2020.

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Citing papers per year: 2021: 94; 2022: 123; 2023: 151; 2024: 139; 2025: 110; 2026: 42.

659 papers.

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  1. 2021Segmenter: Transformer for Semantic Segmentationcited by 855
  2. 2022Language-driven Semantic Segmentationcited by 711
  3. 2023Segment Everything Everywhere All at Oncecited by 546
  4. 2024U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentationcited by 412
  5. 2023VoxFormer: Sparse Voxel Transformer for Camera-based 3D Semantic Scene Completioncited by 306
  6. 2023Segment Anything in Medical Imagescited by 226
  7. 2021A Survey of Human-in-the-loop for Machine Learningcited by 214
  8. 2023A Simple Framework for Open-Vocabulary Segmentation and Detectioncited by 197
  9. 2021Learning Distilled Collaboration Graph for Multi-Agent Perceptioncited by 194
  10. 2024Deep Time Series Models: A Comprehensive Survey and Benchmarkcited by 191
  11. 2022Medical Image Segmentation Review: The success of U-Netcited by 190
  12. 2023Semantic-SAM: Segment and Recognize Anything at Any Granularitycited by 185
38 more
  1. 2023RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Modelcited by 180
  2. 2022V2X-Sim: Multi-Agent Collaborative Perception Dataset and Benchmark for Autonomous Drivingcited by 157
  3. 2023Transformer-Based Visual Segmentation: A Surveycited by 124
  4. 2023Structured Pruning for Deep Convolutional Neural Networks: A surveycited by 122
  5. 2021NeSF: Neural Semantic Fields for Generalizable Semantic Segmentation of 3D Scenescited by 119
  6. 2023A Comprehensive Survey of Dataset Distillationcited by 114
  7. 2021Perturbed and Strict Mean Teachers for Semi-supervised Semantic Segmentationcited by 113
  8. 2023CellViT: Vision Transformers for Precise Cell Segmentation and Classificationcited by 108
  9. 2022Image Data Augmentation for Deep Learning: A Surveycited by 96
  10. 2021Adversarial Sticker: A Stealthy Attack Method in the Physical Worldcited by 92
  11. 2023Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarkscited by 90
  12. 2023LRRNet: A Novel Representation Learning Guided Fusion Network for Infrared and Visible Imagescited by 87
  13. 2021Weakly Supervised Semantic Segmentation by Pixel-to-Prototype Contrastcited by 85
  14. 2022Temporal Action Segmentation: An Analysis of Modern Techniquescited by 85
  15. 2023The Segment Anything Model (SAM) for Remote Sensing Applications: From Zero to One Shotcited by 83
  16. 2024Pay Attention to Your Neighbours: Training-Free Open-Vocabulary Semantic Segmentationcited by 76
  17. 2024Natural Language Understanding and Inference with MLLM in Visual Question Answering: A Surveycited by 74
  18. 2024OMG-Seg: Is One Model Good Enough For All Segmentation?cited by 73
  19. 2021Deep Learning for Instance Retrieval: A Surveycited by 70
  20. 2023A Comprehensive Survey on Applications of Transformers for Deep Learning Taskscited by 70
  21. 2022Open-world Semantic Segmentation via Contrasting and Clustering Vision-Language Embeddingcited by 67
  22. 2022Dense Cross-Query-and-Support Attention Weighted Mask Aggregation for Few-Shot Segmentationcited by 65
  23. 2021Making Images Real Again: A Comprehensive Survey on Deep Image Compositioncited by 63
  24. 2021Evaluating the Robustness of Semantic Segmentation for Autonomous Driving against Real-World Adversarial Patch Attackscited by 55
  25. 2021Beyond Classification: Directly Training Spiking Neural Networks for Semantic Segmentationcited by 54
  26. 2022Discovering Object Masks with Transformers for Unsupervised Semantic Segmentationcited by 54
  27. 2023Segment Anything Model (SAM) Enhanced Pseudo Labels for Weakly Supervised Semantic Segmentationcited by 53
  28. 2023Introduction to Latent Variable Energy-Based Models: A Path Towards Autonomous Machine Intelligencecited by 52
  29. 2021ZeroWaste Dataset: Towards Deformable Object Segmentation in Cluttered Scenescited by 47
  30. 2024UnlearnCanvas: Stylized Image Dataset for Enhanced Machine Unlearning Evaluation in Diffusion Modelscited by 47
  31. 2021Defending against Model Stealing via Verifying Embedded External Featurescited by 44
  32. 2021Look Closer to Segment Better: Boundary Patch Refinement for Instance Segmentationcited by 43
  33. 2021Learning Multiple Dense Prediction Tasks from Partially Annotated Datacited by 42
  34. 2022PseudoClick: Interactive Image Segmentation with Click Imitationcited by 42
  35. 2021Boundary-Aware Segmentation Network for Mobile and Web Applicationscited by 41
  36. 2022Simultaneously Optimizing Perturbations and Positions for Black-box Adversarial Patch Attackscited by 41
  37. 2023MobileSAMv2: Faster Segment Anything to Everythingcited by 41
  38. 2021Active Boundary Loss for Semantic Segmentationcited by 40

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