mlsanityData to 7 Oct 2026

Few-shot Object Detection with Refined Contrastive Learning

arXiv 2211.13495 ↗First posted 24 Nov 2022cs.CV cs.AIcited by 4

Not on the map. Why?

Abstract (arXiv)

Due to the scarcity of sampling data in reality, few-shot object detection (FSOD) has drawn more and more attention because of its ability to quickly train new detection concepts with less data. However, there are still failure identifications due to the difficulty in distinguishing confusable classes. We also notice that the high standard deviation of average precision reveals the inconsistent detection performance. To this end, we propose a novel FSOD method with Refined Contrastive Learning (FSRC). A pre-determination component is introduced to find out the Resemblance Group from novel classes which contains confusable classes. Afterwards, Refined Contrastive Learning (RCL) is pointedly performed on this group of classes in order to increase the inter-class distances among them. In the meantime, the detection results distribute more uniformly which further improve the performance. Experimental results based on PASCAL VOC and COCO datasets demonstrate our proposed method outperforms the current state-of-the-art research.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 52 of its 68 references that are in the map. First posted on arXiv 24 Nov 2022.

Builds on

52 of 68 references.

References · 52

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  3. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  4. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  5. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,482
  6. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
  7. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
  8. 2015SSD: Single Shot MultiBox Detectorcited by 3,125
  9. 2015Fast R-CNNcited by 2,930
  10. 2020Supervised Contrastive Learningcited by 2,803
  11. 2018YOLOv3: An Incremental Improvementcited by 2,652
  12. 2016Matching Networks for One Shot Learningcited by 2,246
38 more
  1. 2021SimCSE: Simple Contrastive Learning of Sentence Embeddingscited by 2,168
  2. 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,160
  3. 2018Unsupervised Feature Learning via Non-Parametric Instance-level Discriminationcited by 1,838
  4. 2019Contrastive Multiview Codingcited by 1,297
  5. 2014Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognitioncited by 719
  6. 2019A Theoretical Analysis of Contrastive Unsupervised Representation Learningcited by 530
  7. 2016R-FCN: Object Detection via Region-based Fully Convolutional Networkscited by 414
  8. 2014Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networkscited by 299
  9. 2020Frustratingly Simple Few-Shot Object Detectioncited by 280
  10. 2018Few-shot Object Detection via Feature Reweightingcited by 265
  11. 2020DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representationscited by 249
  12. 2020InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Trainingcited by 237
  13. 2013OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networkscited by 224
  14. 2019Few-Shot Object Detection with Attention-RPN and Multi-Relation Detectorcited by 206
  15. 2019Meta R-CNN : Towards General Solver for Instance-level Few-shot Learningcited by 182
  16. 2020CERT: Contrastive Self-supervised Learning for Language Understandingcited by 169
  17. 2021FSCE: Few-Shot Object Detection via Contrastive Proposal Encodingcited by 168
  18. 2022Balanced Contrastive Learning for Long-Tailed Visual Recognitioncited by 143
  19. 2020Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wildcited by 142
  20. 2021Targeted Supervised Contrastive Learning for Long-Tailed Recognitioncited by 132
  21. 2021Contrastive Learning based Hybrid Networks for Long-Tailed Image Classificationcited by 130
  22. 2020Multi-Scale Positive Sample Refinement for Few-Shot Object Detectioncited by 116
  23. 2017Unsupervised Learning by Predicting Noisecited by 97
  24. 2021Generalized Few-Shot Object Detection without Forgettingcited by 88
  25. 2018RepMet: Representative-based metric learning for classification and one-shot object detectioncited by 85
  26. 2021Beyond Max-Margin: Class Margin Equilibrium for Few-shot Object Detectioncited by 80
  27. 2021Semantic Relation Reasoning for Shot-Stable Few-Shot Object Detectioncited by 79
  28. 2018LSTD: A Low-Shot Transfer Detector for Object Detectioncited by 77
  29. 2021DETReg: Unsupervised Pretraining with Region Priors for Object Detectioncited by 76
  30. 2021Dense Relation Distillation with Context-aware Aggregation for Few-Shot Object Detectioncited by 53
  31. 2021Hallucination Improves Few-Shot Object Detectioncited by 50
  32. 2020Pretraining with Contrastive Sentence Objectives Improves Discourse Performance of Language Modelscited by 49
  33. 2021Universal-Prototype Enhancing for Few-Shot Object Detectioncited by 43
  34. 2021Label, Verify, Correct: A Simple Few Shot Object Detection Methodcited by 43
  35. 2022Few-Shot Classification with Contrastive Learningcited by 31
  36. 2020Restoring Negative Information in Few-Shot Object Detectioncited by 28
  37. 2018Fine-Grained Visual Categorization using Meta-Learning Optimization with Sample Selection of Auxiliary Datacited by 25
  38. 2022Sylph: A Hypernetwork Framework for Incremental Few-shot Object Detectioncited by 23

50 most cited of 52 All 52 (JSON)

Built on by

Cited by year

Citing papers per year: 2023: 2; 2024: 1; 2025: 1.

4 papers.

  1. 2023Improved Region Proposal Network for Enhanced Few-Shot Object Detectioncited by 5
  2. 2024Cross-domain Multi-modal Few-shot Object Detection via Rich Textcited by 5
  3. 2023Decoupled DETR For Few-shot Object Detectioncited by 1
  4. 2025Cross-domain Few-shot Object Detection with Multi-modal Textual Enrichmentcited by 0