mlsanityData to 7 Oct 2026

Dual Progressive Transformations for Weakly Supervised Semantic Segmentation

arXiv 2209.15211 ↗First posted 30 Sep 2022cs.CVcited by 4

Not on the map. Why?

Abstract (arXiv)

Weakly supervised semantic segmentation (WSSS), which aims to mine the object regions by merely using class-level labels, is a challenging task in computer vision. The current state-of-the-art CNN-based methods usually adopt Class-Activation-Maps (CAMs) to highlight the potential areas of the object, however, they may suffer from the part-activated issues. To this end, we try an early attempt to explore the global feature attention mechanism of vision transformer in WSSS task. However, since the transformer lacks the inductive bias as in CNN models, it can not boost the performance directly and may yield the over-activated problems. To tackle these drawbacks, we propose a Convolutional Neural Networks Refined Transformer (CRT) to mine a globally complete and locally accurate class activation maps in this paper. To validate the effectiveness of our proposed method, extensive experiments are conducted on PASCAL VOC 2012 and CUB-200-2011 datasets. Experimental evaluations show that our proposed CRT achieves the new state-of-the-art performance on both the weakly supervised semantic segmentation task the weakly supervised object localization task, which outperform others by a large margin.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 51 of its 60 references that are in the map. First posted on arXiv 30 Sep 2022.

Builds on

51 of 60 references.

References · 51

  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. 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,893
  5. 2020Bootstrap your own latent: A new approach to self-supervised Learningcited by 4,712
  6. 2020Training data-efficient image transformers & distillation through attentioncited by 4,560
  7. 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,239
  8. 2020Exploring Simple Siamese Representation Learningcited by 2,853
  9. 2015Learning Deep Features for Discriminative Localizationcited by 2,430
  10. 2020Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformerscited by 1,368
  11. 2022Exploring Plain Vision Transformer Backbones for Object Detectioncited by 723
  12. 2022ViTPose: Simple Vision Transformer Baselines for Human Pose Estimationcited by 450
38 more
  1. 2016ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentationcited by 330
  2. 2018Learning Pixel-level Semantic Affinity with Image-level Supervision for Weakly Supervised Semantic Segmentationcited by 321
  3. 2020Self-supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentationcited by 299
  4. 2015What's the Point: Semantic Segmentation with Point Supervisioncited by 297
  5. 2019Weakly Supervised Learning of Instance Segmentation with Inter-pixel Relationscited by 245
  6. 2015BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentationcited by 238
  7. 2020Causal Intervention for Weakly-Supervised Semantic Segmentationcited by 229
  8. 2016Simple Does It: Weakly Supervised Instance and Semantic Segmentationcited by 226
  9. 2017Hide-and-Seek: Forcing a Network to be Meticulous for Weakly-supervised Object and Action Localizationcited by 207
  10. 2017Object Region Mining with Adversarial Erasing: A Simple Classification to Semantic Segmentation Approachcited by 201
  11. 2015Weakly- and Semi-Supervised Learning of a DCNN for Semantic Image Segmentationcited by 189
  12. 2018Tell Me Where to Look: Guided Attention Inference Networkcited by 168
  13. 2018Adversarial Complementary Learning for Weakly Supervised Object Localizationcited by 166
  14. 2020Weakly-Supervised Semantic Segmentation via Sub-category Explorationcited by 145
  15. 2020Mining Cross-Image Semantics for Weakly Supervised Semantic Segmentationcited by 144
  16. 2022Multi-class Token Transformer for Weakly Supervised Semantic Segmentationcited by 138
  17. 2021Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic Segmentationcited by 134
  18. 2021Railroad is not a Train: Saliency as Pseudo-pixel Supervision for Weakly Supervised Semantic Segmentationcited by 127
  19. 2019Attention-based Dropout Layer for Weakly Supervised Object Localizationcited by 125
  20. 2021TS-CAM: Token Semantic Coupled Attention Map for Weakly Supervised Object Localizationcited by 122
  21. 2018Learning random-walk label propagation for weakly-supervised semantic segmentationcited by 101
  22. 2021Non-Salient Region Object Mining for Weakly Supervised Semantic Segmentationcited by 95
  23. 2019Reliability Does Matter: An End-to-End Weakly Supervised Semantic Segmentation Approachcited by 88
  24. 2021Leveraging Auxiliary Tasks with Affinity Learning for Weakly Supervised Semantic Segmentationcited by 77
  25. 2021Complementary Patch for Weakly Supervised Semantic Segmentationcited by 75
  26. 2022Class Re-Activation Maps for Weakly-Supervised Semantic Segmentationcited by 75
  27. 2021Context Decoupling Augmentation for Weakly Supervised Semantic Segmentationcited by 69
  28. 2020Three Ways to Improve Semantic Segmentation with Self-Supervised Depth Estimationcited by 66
  29. 2021Reducing Information Bottleneck for Weakly Supervised Semantic Segmentationcited by 65
  30. 2022L2G: A Simple Local-to-Global Knowledge Transfer Framework for Weakly Supervised Semantic Segmentationcited by 57
  31. 2021SeMask: Semantically Masked Transformers for Semantic Segmentationcited by 55
  32. 2022A Unified Transformer Framework for Group-based Segmentation: Co-Segmentation, Co-Saliency Detection and Video Salient Object Detectioncited by 51
  33. 2021Puzzle-CAM: Improved localization via matching partial and full featurescited by 37
  34. 2022Threshold Matters in WSSS: Manipulating the Activation for the Robust and Accurate Segmentation Model Against Thresholdscited by 37
  35. 2021Activation Modulation and Recalibration Scheme for Weakly Supervised Semantic Segmentationcited by 29
  36. 2021Uncertainty Estimation via Response Scaling for Pseudo-mask Noise Mitigation in Weakly-supervised Semantic Segmentationcited by 28
  37. 2020Mixup-CAM: Weakly-supervised Semantic Segmentation via Uncertainty Regularizationcited by 24
  38. 2022Weakly-Supervised Semantic Segmentation with Visual Words Learning and Hybrid Poolingcited by 19

50 most cited of 51 All 51 (JSON)

Built on by

Cited by year

Citing papers per year: 2023: 4.

4 papers.

  1. 2023Occlusion-Aware Detection and Re-ID Calibrated Network for Multi-Object Trackingcited by 2
  2. 2023Semantic-Constraint Matching Transformer for Weakly Supervised Object Localizationcited by 2
  3. 2023COMNet: Co-Occurrent Matching for Weakly Supervised Semantic Segmentationcited by 0
  4. 2023Dual-Augmented Transformer Network for Weakly Supervised Semantic Segmentationcited by 0