mlsanityData to 7 Oct 2026

A Unified Framework for Attention-Based Few-Shot Object Detection

arXiv 2201.02052 ↗First posted 6 Jan 2022cs.CVcited by 1

Not on the map. Why?

Abstract (arXiv)

Few-Shot Object Detection (FSOD) is a rapidly growing field in computer vision. It consists in finding all occurrences of a given set of classes with only a few annotated examples for each class. Numerous methods have been proposed to address this challenge and most of them are based on attention mechanisms. However, the great variety of classic object detection frameworks and training strategies makes performance comparison between methods difficult. In particular, for attention-based FSOD methods, it is laborious to compare the impact of the different attention mechanisms on performance. This paper aims at filling this shortcoming. To do so, a flexible framework is proposed to allow the implementation of most of the attention techniques available in the literature. To properly introduce such a framework, a detailed review of the existing FSOD methods is firstly provided. Some different attention mechanisms are then reimplemented within the framework and compared with all other parameters fixed.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 44 of its 53 references that are in the map. First posted on arXiv 6 Jan 2022.

Builds on

44 of 53 references.

References · 44

  1. 2017Attention Is All You Needcited by 59,932
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  3. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  4. 2020End-to-End Object Detection with Transformerscited by 7,384
  5. 2017Mask R-CNNcited by 7,259
  6. 2016Feature Pyramid Networks for Object Detectioncited by 5,021
  7. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  8. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
  9. 2017Squeeze-and-Excitation Networkscited by 4,763
  10. 2016Overcoming catastrophic forgetting in neural networkscited by 4,718
  11. 2017Prototypical Networks for Few-shot Learningcited by 3,001
  12. 2015Fast R-CNNcited by 2,930
32 more
  1. 2017Non-local Neural Networkscited by 2,290
  2. 2016Matching Networks for One Shot Learningcited by 2,246
  3. 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,160
  4. 2019FCOS: Fully Convolutional One-Stage Object Detectioncited by 1,573
  5. 2017Learning to Compare: Relation Network for Few-Shot Learningcited by 1,139
  6. 2018CornerNet: Detecting Objects as Paired Keypointscited by 763
  7. 2019CenterNet: Keypoint Triplets for Object Detectioncited by 654
  8. 2019Generalizing from a Few Examples: A Survey on Few-Shot Learningcited by 462
  9. 2020Frustratingly Simple Few-Shot Object Detectioncited by 280
  10. 2018Few-shot Object Detection via Feature Reweightingcited by 265
  11. 2019Few-Shot Object Detection with Attention-RPN and Multi-Relation Detectorcited by 206
  12. 2021Dynamic Head: Unifying Object Detection Heads with Attentionscited by 185
  13. 2020CrossTransformers: spatially-aware few-shot transfercited by 174
  14. 2021FSCE: Few-Shot Object Detection via Contrastive Proposal Encodingcited by 168
  15. 2020Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wildcited by 142
  16. 2020Multi-Scale Positive Sample Refinement for Few-Shot Object Detectioncited by 116
  17. 2016Learning feed-forward one-shot learnerscited by 93
  18. 2021Generalized Few-Shot Object Detection without Forgettingcited by 88
  19. 2018RepMet: Representative-based metric learning for classification and one-shot object detectioncited by 85
  20. 2018LSTD: A Low-Shot Transfer Detector for Object Detectioncited by 77
  21. 2019One-Shot Object Detection with Co-Attention and Co-Excitationcited by 67
  22. 2021Meta Faster R-CNN: Towards Accurate Few-Shot Object Detection with Attentive Feature Alignmentcited by 57
  23. 2021Meta-DETR: Image-Level Few-Shot Object Detection with Inter-Class Correlation Exploitationcited by 44
  24. 2021Universal-Prototype Enhancing for Few-Shot Object Detectioncited by 43
  25. 2021A Survey of Self-Supervised and Few-Shot Object Detectioncited by 35
  26. 2020Few-shot Object Detection on Remote Sensing Imagescited by 23
  27. 2020Few-shot Object Detection with Self-adaptive Attention Network for Remote Sensing Imagescited by 14
  28. 2020Few-Shot Object Detection via Knowledge Transfercited by 13
  29. 2020One-Shot Object Detection without Fine-Tuningcited by 11
  30. 2021A Comparative Review of Recent Few-Shot Object Detection Algorithmscited by 10
  31. 2021Dynamic Relevance Learning for Few-Shot Object Detectioncited by 5
  32. 2021Experience feedback using Representation Learning for Few-Shot Object Detection on Aerial Imagescited by 3

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Few-Shot Learning in Video and 3D Object Detection: A Surveycited by 2