mlsanityData to 14 Oct 2026

CATrans: Context and Affinity Transformer for Few-Shot Segmentation

arXiv 2204.12817 ↗First posted 27 Apr 2022cs.CVcited by 7

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Abstract (arXiv)

Few-shot segmentation (FSS) aims to segment novel categories given scarce annotated support images. The crux of FSS is how to aggregate dense correlations between support and query images for query segmentation while being robust to the large variations in appearance and context. To this end, previous Transformer-based methods explore global consensus either on context similarity or affinity map between support-query pairs. In this work, we effectively integrate the context and affinity information via the proposed novel Context and Affinity Transformer (CATrans) in a hierarchical architecture. Specifically, the Relation-guided Context Transformer (RCT) propagates context information from support to query images conditioned on more informative support features. Based on the observation that a huge feature distinction between support and query pairs brings barriers for context knowledge transfer, the Relation-guided Affinity Transformer (RAT) measures attention-aware affinity as auxiliary information for FSS, in which the self-affinity is responsible for more reliable cross-affinity. We conduct experiments to demonstrate the effectiveness of the proposed model, outperforming the state-of-the-art methods.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 18 of its 25 references that are in the map. First posted on arXiv 27 Apr 2022.

Builds on

18 of 25 references.

References · 18

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  3. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  4. 2020End-to-End Object Detection with Transformerscited by 7,414
  5. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,859
  6. 2020Deformable DETR: Deformable Transformers for End-to-End Object Detectioncited by 3,422
  7. 2016RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentationcited by 465
  8. 2019PANet: Few-Shot Image Semantic Segmentation with Prototype Alignmentcited by 449
  9. 2017One-Shot Learning for Semantic Segmentationcited by 337
  10. 2020Prior Guided Feature Enrichment Network for Few-Shot Segmentationcited by 271
  11. 2019CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learningcited by 217
  12. 2020Prototype Mixture Models for Few-shot Semantic Segmentationcited by 190
6 more
  1. 2019Feature Weighting and Boosting for Few-Shot Segmentationcited by 189
  2. 2018SG-One: Similarity Guidance Network for One-Shot Semantic Segmentationcited by 169
  3. 2020Part-aware Prototype Network for Few-shot Semantic Segmentationcited by 162
  4. 2021Few-Shot Segmentation via Cycle-Consistent Transformercited by 133
  5. 2021Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformercited by 116
  6. 2021Dense Gaussian Processes for Few-Shot Segmentationcited by 17

Built on by

Cited by year

Citing papers per year: 2023: 5; 2024: 1; 2025: 1.

7 papers.

  1. 2023Learning Context-aware Classifier for Semantic Segmentationcited by 23
  2. 2023Visual and Textual Prior Guided Mask Assemble for Few-Shot Segmentation and Beyondcited by 19
  3. 2023Learning Cross-Modal Affinity for Referring Video Object Segmentation Targeting Limited Samplescited by 6
  4. 2023Dense Affinity Matching for Few-Shot Segmentationcited by 5
  5. 2025Few-Shot Referring Video Single- and Multi-Object Segmentation via Cross-Modal Affinity with Instance Sequence Matchingcited by 5
  6. 2024Group-On: Boosting One-Shot Segmentation with Supportive Querycited by 3
  7. 2023Multi-Content Interaction Network for Few-Shot Segmentationcited by 2