mlsanityData to 14 Oct 2026

RepMet: Representative-based metric learning for classification and one-shot object detection

arXiv 1806.04728 ↗First posted 12 Jun 2018cs.CVcited by 85

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Abstract (arXiv)

Distance metric learning (DML) has been successfully applied to object classification, both in the standard regime of rich training data and in the few-shot scenario, where each category is represented by only a few examples. In this work, we propose a new method for DML that simultaneously learns the backbone network parameters, the embedding space, and the multi-modal distribution of each of the training categories in that space, in a single end-to-end training process. Our approach outperforms state-of-the-art methods for DML-based object classification on a variety of standard fine-grained datasets. Furthermore, we demonstrate the effectiveness of our approach on the problem of few-shot object detection, by incorporating the proposed DML architecture as a classification head into a standard object detection model. We achieve the best results on the ImageNet-LOC dataset compared to strong baselines, when only a few training examples are available. We also offer the community a new episodic benchmark based on the ImageNet dataset for the few-shot object detection task.

Key facts. Cited by 85 papers in the map (data through 14 Oct 2026). First posted on arXiv 12 Jun 2018.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 32; 2022: 27; 2023: 14; 2024: 6; 2025: 4; 2026: 2.

85 papers.

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  1. 2021FSCE: Few-Shot Object Detection via Contrastive Proposal Encodingcited by 168
  2. 2021DeFRCN: Decoupled Faster R-CNN for Few-Shot Object Detectioncited by 106
  3. 2023Vision-Language Models in Remote Sensing: Current Progress and Future Trendscited by 93
  4. 2021Generalized Few-Shot Object Detection without Forgettingcited by 88
  5. 2022Learning from Few Examples: A Summary of Approaches to Few-Shot Learningcited by 86
  6. 2022OSOP: A Multi-Stage One Shot Object Pose Estimation Frameworkcited by 81
  7. 2021Semantic Relation Reasoning for Shot-Stable Few-Shot Object Detectioncited by 79
  8. 2021DETReg: Unsupervised Pretraining with Region Priors for Object Detectioncited by 76
  9. 2021Meta Faster R-CNN: Towards Accurate Few-Shot Object Detection with Attentive Feature Alignmentcited by 57
  10. 2021Query Adaptive Few-Shot Object Detection with Heterogeneous Graph Convolutional Networkscited by 55
  11. 2021Dense Relation Distillation with Context-aware Aggregation for Few-Shot Object Detectioncited by 53
  12. 2021Spatio-temporal Relation Modeling for Few-shot Action Recognitioncited by 53
38 more
  1. 2021Hallucination Improves Few-Shot Object Detectioncited by 50
  2. 2021Universal-Prototype Enhancing for Few-Shot Object Detectioncited by 43
  3. 2021Label, Verify, Correct: A Simple Few Shot Object Detection Methodcited by 43
  4. 2021Few-Shot Object Detection via Association and DIscriminationcited by 39
  5. 2021A Survey of Self-Supervised and Few-Shot Object Detectioncited by 35
  6. 2021Dual-Awareness Attention for Few-Shot Object Detectioncited by 29
  7. 2021Few-Shot Object Detection: A Comprehensive Surveycited by 29
  8. 2023Generating Features with Increased Crop-related Diversity for Few-Shot Object Detectioncited by 21
  9. 2022iFS-RCNN: An Incremental Few-shot Instance Segmentercited by 19
  10. 2022Active Learning Strategies for Weakly-supervised Object Detectioncited by 17
  11. 2021A Unified Framework for Generalized Low-Shot Medical Image Segmentation with Scarce Datacited by 16
  12. 2021Few-Shot Learning for Road Object Detectioncited by 13
  13. 2021Class-Incremental Few-Shot Object Detectioncited by 13
  14. 2022Semantic-aligned Fusion Transformer for One-shot Object Detectioncited by 13
  15. 2022Disentangle and Remerge: Interventional Knowledge Distillation for Few-Shot Object Detection from A Conditional Causal Perspectivecited by 13
  16. 2022Breaking Immutable: Information-Coupled Prototype Elaboration for Few-Shot Object Detectioncited by 12
  17. 2023Reference Twice: A Simple and Unified Baseline for Few-Shot Instance Segmentationcited by 12
  18. 2022Multi-Modal Few-Shot Object Detection with Meta-Learning-Based Cross-Modal Promptingcited by 11
  19. 2022Multi-Faceted Distillation of Base-Novel Commonality for Few-shot Object Detectioncited by 11
  20. 2021A Comparative Review of Recent Few-Shot Object Detection Algorithmscited by 10
  21. 2021MFNet: Multi-class Few-shot Segmentation Network with Pixel-wise Metric Learningcited by 10
  22. 2022Time-rEversed diffusioN tEnsor Transformer: A new TENET of Few-Shot Object Detectioncited by 10
  23. 2023Adaptive FSS: A Novel Few-Shot Segmentation Framework via Prototype Enhancementcited by 10
  24. 2024Improved Diversity-Promoting Collaborative Metric Learning for Recommendationcited by 9
  25. 2022Mobile Robot Manipulation using Pure Object Detectioncited by 8
  26. 2022Recent Few-Shot Object Detection Algorithms: A Survey with Performance Comparisoncited by 8
  27. 2024SMILe: Leveraging Submodular Mutual Information For Robust Few-Shot Object Detectioncited by 8
  28. 2021Few-Shot Video Object Detectioncited by 7
  29. 2023Identification of Novel Classes for Improving Few-Shot Object Detectioncited by 7
  30. 2021A Survey of Deep Learning for Low-Shot Object Detectioncited by 6
  31. 2025Generalized Semantic Contrastive Learning via Embedding Side Information for Few-Shot Object Detectioncited by 6
  32. 2021Dynamic Relevance Learning for Few-Shot Object Detectioncited by 5
  33. 2022Task-Adaptive Feature Transformer with Semantic Enrichment for Few-Shot Segmentationcited by 5
  34. 2022CFA: Constraint-based Finetuning Approach for Generalized Few-Shot Object Detectioncited by 5
  35. 2023Transformation-Invariant Network for Few-Shot Object Detection in Remote Sensing Imagescited by 5
  36. 2023Improved Region Proposal Network for Enhanced Few-Shot Object Detectioncited by 5
  37. 2023Few-shot Object Detection in Remote Sensing: Lifting the Curse of Incompletely Annotated Novel Objectscited by 5
  38. 2022Few-shot Object Detection with Refined Contrastive Learningcited by 4

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