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Emerging Trends in Pseudo-Label Refinement for Weakly Supervised Semantic Segmentation with Image-Level Supervision

arXiv 2507.21587 ↗First posted 29 Jul 2025cs.CVcited by 0

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Abstract (arXiv)

Unlike fully supervised semantic segmentation, weakly supervised semantic segmentation (WSSS) relies on weaker forms of supervision to perform dense prediction tasks. Among the various types of weak supervision, WSSS with image level annotations is considered both the most challenging and the most practical, attracting significant research attention. Therefore, in this review, we focus on WSSS with image level annotations. Additionally, this review concentrates on mainstream research directions, deliberately omitting less influential branches. Given the rapid development of new methods and the limitations of existing surveys in capturing recent trends, there is a pressing need for an updated and comprehensive review. Our goal is to fill this gap by synthesizing the latest advancements and state-of-the-art techniques in WSSS with image level labels. Basically, we provide a comprehensive review of recent advancements in WSSS with image level labels, categorizing existing methods based on the types and levels of additional supervision involved. We also examine the challenges of applying advanced methods to domain specific datasets in WSSS,a topic that remains underexplored. Finally, we discuss the current challenges, evaluate the limitations of existing approaches, and outline several promising directions for future research. This review is intended for researchers who are already familiar with the fundamental concepts of WSSS and are seeking to deepen their understanding of current advances and methodological innovations.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 27 of its 41 references that are in the map. First posted on arXiv 29 Jul 2025.

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27 of 41 references.

References · 27

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  5. 2022CLIP is Also an Efficient Segmenter: A Text-Driven Approach for Weakly Supervised Semantic Segmentationcited by 118
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  10. 2023Token Contrast for Weakly-Supervised Semantic Segmentationcited by 65
  11. 2022Regional Semantic Contrast and Aggregation for Weakly Supervised Semantic Segmentationcited by 64
  12. 2023Segment Anything Model (SAM) Enhanced Pseudo Labels for Weakly Supervised Semantic Segmentationcited by 53
15 more
  1. 2023Foundation Model Assisted Weakly Supervised Semantic Segmentationcited by 33
  2. 2023Extracting Class Activation Maps from Non-Discriminative Features as wellcited by 30
  3. 2023Weakly-Supervised Semantic Segmentation with Image-Level Labels: from Traditional Models to Foundation Modelscited by 19
  4. 2024DuPL: Dual Student with Trustworthy Progressive Learning for Robust Weakly Supervised Semantic Segmentationcited by 17
  5. 2025Prototype-Based Image Prompting for Weakly Supervised Histopathological Image Segmentationcited by 17
  6. 2024Hunting Attributes: Context Prototype-Aware Learning for Weakly Supervised Semantic Segmentationcited by 16
  7. 2024Prompt Categories Cluster for Weakly Supervised Semantic Segmentationcited by 16
  8. 2024Frozen CLIP: A Strong Backbone for Weakly Supervised Semantic Segmentationcited by 15
  9. 2023USAGE: A Unified Seed Area Generation Paradigm for Weakly Supervised Semantic Segmentationcited by 14
  10. 2024Exploring Low-Resource Medical Image Classification with Weakly Supervised Prompt Learningcited by 10
  11. 2024MoRe: Class Patch Attention Needs Regularization for Weakly Supervised Semantic Segmentationcited by 9
  12. 2023Weakly Supervised Semantic Segmentation for Driving Scenescited by 6
  13. 2023Prompting classes: Exploring the Power of Prompt Class Learning in Weakly Supervised Semantic Segmentationcited by 3
  14. 2023All-pairs Consistency Learning for Weakly Supervised Semantic Segmentationcited by 3
  15. 2024Precision matters: Precision-aware ensemble for weakly supervised semantic segmentationcited by 2

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