mlsanityData to 7 Oct 2026

StructToken : Rethinking Semantic Segmentation with Structural Prior

arXiv 2203.12612 ↗First posted 23 Mar 2022cs.CVcited by 30

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Abstract (arXiv)

In previous deep-learning-based methods, semantic segmentation has been regarded as a static or dynamic per-pixel classification task, textiti.e., classify each pixel representation to a specific category. However, these methods only focus on learning better pixel representations or classification kernels while ignoring the structural information of objects, which is critical to human decision-making mechanism. In this paper, we present a new paradigm for semantic segmentation, named structure-aware extraction. Specifically, it generates the segmentation results via the interactions between a set of learned structure tokens and the image feature, which aims to progressively extract the structural information of each category from the feature. Extensive experiments show that our StructToken outperforms the state-of-the-art on three widely-used benchmarks, including ADE20K, Cityscapes, and COCO-Stuff-10K.

Key facts. Cited by 30 papers in the map (data through 7 Oct 2026). Builds on 48 of its 66 references that are in the map. First posted on arXiv 23 Mar 2022.

Builds on

48 of 66 references.

References · 48

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  4. 2016Feature Pyramid Networks for Object Detectioncited by 5,021
  5. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  6. 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,427
  7. 2020Deformable DETR: Deformable Transformers for End-to-End Object Detectioncited by 3,410
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  10. 2021SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformerscited by 2,923
  11. 2016Pyramid Scene Parsing Networkcited by 2,472
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36 more
  1. 2017Rethinking Atrous Convolution for Semantic Image Segmentationcited by 2,154
  2. 2016Identity Mappings in Deep Residual Networkscited by 1,960
  3. 2021Vision Transformers for Dense Predictioncited by 1,789
  4. 2020Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformerscited by 1,368
  5. 2018Unified Perceptual Parsing for Scene Understandingcited by 1,265
  6. 2021Per-Pixel Classification is Not All You Need for Semantic Segmentationcited by 1,148
  7. 2016Semantic Understanding of Scenes through the ADE20K Datasetcited by 1,063
  8. 2018Dual Attention Network for Scene Segmentationcited by 1,001
  9. 2016COCO-Stuff: Thing and Stuff Classes in Contextcited by 858
  10. 2021Segmenter: Transformer for Semantic Segmentationcited by 855
  11. 2018CCNet: Criss-Cross Attention for Semantic Segmentationcited by 717
  12. 2019Panoptic Feature Pyramid Networkscited by 644
  13. 2019Segmentation Transformer: Object-Contextual Representations for Semantic Segmentationcited by 573
  14. 2021How to train your ViT? Data, Augmentation, and Regularization in Vision Transformerscited by 562
  15. 2019GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyondcited by 331
  16. 2019Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challengescited by 313
  17. 2018Context Encoding for Semantic Segmentationcited by 289
  18. 2019Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentationcited by 258
  19. 2021K-Net: Towards Unified Image Segmentationcited by 211
  20. 2019Asymmetric Non-local Neural Networks for Semantic Segmentationcited by 196
  21. 2019ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blockscited by 178
  22. 2019Expectation-Maximization Attention Networks for Semantic Segmentationcited by 147
  23. 2020Disentangled Non-Local Neural Networkscited by 132
  24. 2020Feature Pyramid Transformercited by 94
  25. 2020Learning Dynamic Routing for Semantic Segmentationcited by 79
  26. 2019Semantic Correlation Promoted Shape-Variant Context for Segmentationcited by 61
  27. 2021Learning Statistical Texture for Semantic Segmentationcited by 50
  28. 2021Mining Contextual Information Beyond Image for Semantic Segmentationcited by 48
  29. 2020Spatial Pyramid Based Graph Reasoning for Semantic Segmentationcited by 47
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  33. 2021Fully Transformer Networks for Semantic Image Segmentationcited by 24
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 4; 2023: 12; 2024: 5; 2025: 7; 2026: 2.

30 papers.

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  1. 2022Vision Transformer Adapter for Dense Predictionscited by 564
  2. 2023DDP: Diffusion Model for Dense Visual Predictioncited by 148
  3. 2023Transformer-Based Visual Segmentation: A Surveycited by 124
  4. 2022SegViT: Semantic Segmentation with Plain Vision Transformerscited by 105
  5. 2023SegViTv2: Exploring Efficient and Continual Semantic Segmentation with Plain Vision Transformerscited by 24
  6. 2023UniNeXt: Exploring A Unified Architecture for Vision Recognitioncited by 16
  7. 2025Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentationcited by 11
  8. 2024Bring Adaptive Binding Prototypes to Generalized Referring Expression Segmentationcited by 8
  9. 2022Representation Separation for Semantic Segmentation with Vision Transformerscited by 4
  10. 2023PRSeg: A Lightweight Patch Rotate MLP Decoder for Semantic Segmentationcited by 4
  11. 2024Deep Common Feature Mining for Efficient Video Semantic Segmentationcited by 4
  12. 2024DSNet: A Novel Way to Use Atrous Convolutions in Semantic Segmentationcited by 4
18 more
  1. 2024Fine-grained Background Representation for Weakly Supervised Semantic Segmentationcited by 4
  2. 2023Category Feature Transformer for Semantic Segmentationcited by 3
  3. 2023Boosting Semantic Segmentation from the Perspective of Explicit Class Embeddingscited by 3
  4. 2023Minimalist and High-Performance Semantic Segmentation with Plain Vision Transformerscited by 3
  5. 2022Domain-invariant Prototypes for Semantic Segmentationcited by 2
  6. 2023CARD: Semantic Segmentation with Efficient Class-Aware Regularized Decodercited by 2
  7. 2023High-level Feature Guided Decoding for Semantic Segmentationcited by 2
  8. 2023Exploring vision transformer layer choosing for semantic segmentationcited by 1
  9. 2023AxWin Transformer: A Context-Aware Vision Transformer Backbone with Axial Windowscited by 1
  10. 2025VPNeXt -- Rethinking Dense Decoding for Plain Vision Transformercited by 1
  11. 2025Segment Any Vehicle: Semantic and Visual Context Driven SAM and A Benchmarkcited by 1
  12. 2025Deep Learning for Crack Detection: A Review of Learning Paradigms, Generalizability, and Datasetscited by 1
  13. 2025I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentationcited by 1
  14. 2024Towards Customized Knowledge Distillation for Chip-Level Dense Image Predictionscited by 0
  15. 2025Is Pre-training Applicable to the Decoder for Dense Prediction?cited by 0
  16. 2025Classifier Enhancement Using Extended Context and Domain Experts for Semantic Segmentationcited by 0
  17. 2026Token-Space Mask Prediction for Efficient Vision Transformer Segmentationcited by 0
  18. 2026Decoder-Agnostic Token Merging for Vision Transformers: A Systematic Study of G2TMcited by 0