mlsanityData to 7 Oct 2026

tSF: Transformer-based Semantic Filter for Few-Shot Learning

arXiv 2211.00868 ↗First posted 2 Nov 2022cs.CVcited by 6

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Abstract (arXiv)

Few-Shot Learning (FSL) alleviates the data shortage challenge via embedding discriminative target-aware features among plenty seen (base) and few unseen (novel) labeled samples. Most feature embedding modules in recent FSL methods are specially designed for corresponding learning tasks (e.g., classification, segmentation, and object detection), which limits the utility of embedding features. To this end, we propose a light and universal module named transformer-based Semantic Filter (tSF), which can be applied for different FSL tasks. The proposed tSF redesigns the inputs of a transformer-based structure by a semantic filter, which not only embeds the knowledge from whole base set to novel set but also filters semantic features for target category. Furthermore, the parameters of tSF is equal to half of a standard transformer block (less than 1M). In the experiments, our tSF is able to boost the performances in different classic few-shot learning tasks (about 2% improvement), especially outperforms the state-of-the-arts on multiple benchmark datasets in few-shot classification task.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 65 of its 79 references that are in the map. First posted on arXiv 2 Nov 2022.

Builds on

65 of 79 references.

References · 65

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  4. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  5. 2020End-to-End Object Detection with Transformerscited by 7,384
  6. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  7. 2020Training data-efficient image transformers & distillation through attentioncited by 4,560
  8. 2020Deformable DETR: Deformable Transformers for End-to-End Object Detectioncited by 3,410
  9. 2017Prototypical Networks for Few-shot Learningcited by 3,001
  10. 2021SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformerscited by 2,923
  11. 2016Matching Networks for One Shot Learningcited by 2,246
  12. 2021Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutionscited by 1,960
38 more
  1. 2019SuperGlue: Learning Feature Matching with Graph Neural Networkscited by 1,437
  2. 2020Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformerscited by 1,368
  3. 2017Random Erasing Data Augmentationcited by 1,235
  4. 2017Learning to Compare: Relation Network for Few-Shot Learningcited by 1,139
  5. 2018On First-Order Meta-Learning Algorithmscited by 1,003
  6. 2021LoFTR: Detector-Free Local Feature Matching with Transformerscited by 974
  7. 2016Learning to learn by gradient descent by gradient descentcited by 616
  8. 2018Meta-Learning with Latent Embedding Optimizationcited by 476
  9. 2018Meta-Learning for Semi-Supervised Few-Shot Classificationcited by 456
  10. 2019PANet: Few-Shot Image Semantic Segmentation with Prototype Alignmentcited by 449
  11. 2020Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need?cited by 435
  12. 2017One-Shot Learning for Semantic Segmentationcited by 337
  13. 2018Few-Shot Learning via Embedding Adaptation with Set-to-Set Functionscited by 293
  14. 2020Frustratingly Simple Few-Shot Object Detectioncited by 280
  15. 2020Prior Guided Feature Enrichment Network for Few-Shot Segmentationcited by 271
  16. 2017Meta Networkscited by 268
  17. 2018Few-shot Object Detection via Feature Reweightingcited by 265
  18. 2019Cross Attention Network for Few-shot Classificationcited by 218
  19. 2019CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learningcited by 217
  20. 2019Few-Shot Object Detection with Attention-RPN and Multi-Relation Detectorcited by 206
  21. 2019SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learningcited by 194
  22. 2021Hypercorrelation Squeeze for Few-Shot Segmentationcited by 193
  23. 2020Prototype Mixture Models for Few-shot Semantic Segmentationcited by 190
  24. 2019Feature Weighting and Boosting for Few-Shot Segmentationcited by 189
  25. 2019Meta R-CNN : Towards General Solver for Instance-level Few-shot Learningcited by 182
  26. 2020CrossTransformers: spatially-aware few-shot transfercited by 174
  27. 2018SG-One: Similarity Guidance Network for One-Shot Semantic Segmentationcited by 169
  28. 2021FSCE: Few-Shot Object Detection via Contrastive Proposal Encodingcited by 168
  29. 2020Part-aware Prototype Network for Few-shot Semantic Segmentationcited by 162
  30. 2020Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wildcited by 142
  31. 2019Boosting Few-Shot Visual Learning with Self-Supervisioncited by 140
  32. 2020Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You Need?cited by 135
  33. 2020Multi-Scale Positive Sample Refinement for Few-Shot Object Detectioncited by 116
  34. 2021Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformercited by 116
  35. 2021DeFRCN: Decoupled Faster R-CNN for Few-Shot Object Detectioncited by 106
  36. 2020CRNet: Cross-Reference Networks for Few-Shot Segmentationcited by 101
  37. 2020Feature Pyramid Transformercited by 94
  38. 2016Learning feed-forward one-shot learnerscited by 93

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Citing papers per year: 2023: 5; 2024: 0; 2025: 1.

6 papers.

  1. 2023Context-Aware Meta-Learningcited by 20
  2. 2023SpatialFormer: Semantic and Target Aware Attentions for Few-Shot Learningcited by 4
  3. 2023Clustered-patch Element Connection for Few-shot Learningcited by 4
  4. 2023Disentangled Representation Learning with Transmitted Information Bottleneckcited by 3
  5. 2025Personalized OVSS: Understanding Personal Concept in Open-Vocabulary Semantic Segmentationcited by 1
  6. 2023Recent Advances of Local Mechanisms in Computer Vision: A Survey and Outlook of Recent Workcited by 0