mlsanityData to 14 Oct 2026

Query Adaptive Few-Shot Object Detection with Heterogeneous Graph Convolutional Networks

arXiv 2112.09791 ↗First posted 17 Dec 2021cs.CV cs.AI cs.MMcited by 55

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Abstract (arXiv)

Few-shot object detection (FSOD) aims to detect never-seen objects using few examples. This field sees recent improvement owing to the meta-learning techniques by learning how to match between the query image and few-shot class examples, such that the learned model can generalize to few-shot novel classes. However, currently, most of the meta-learning-based methods perform pairwise matching between query image regions (usually proposals) and novel classes separately, therefore failing to take into account multiple relationships among them. In this paper, we propose a novel FSOD model using heterogeneous graph convolutional networks. Through efficient message passing among all the proposal and class nodes with three different types of edges, we could obtain context-aware proposal features and query-adaptive, multiclass-enhanced prototype representations for each class, which could help promote the pairwise matching and improve final FSOD accuracy. Extensive experimental results show that our proposed model, denoted as QA-FewDet, outperforms the current state-of-the-art approaches on the PASCAL VOC and MSCOCO FSOD benchmarks under different shots and evaluation metrics.

Key facts. Cited by 55 papers in the map (data through 14 Oct 2026). Builds on 37 of its 45 references that are in the map. First posted on arXiv 17 Dec 2021.

Builds on

37 of 45 references.

References · 37

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  3. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  4. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  5. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
  6. 2017Graph Attention Networkscited by 8,923
  7. 2017Mask R-CNNcited by 7,277
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  9. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
  10. 2015SSD: Single Shot MultiBox Detectorcited by 3,130
  11. 2017Prototypical Networks for Few-shot Learningcited by 3,015
  12. 2015Fast R-CNNcited by 2,933
25 more
  1. 2016Matching Networks for One Shot Learningcited by 2,255
  2. 2019FCOS: Fully Convolutional One-Stage Object Detectioncited by 1,577
  3. 2018Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognitioncited by 1,209
  4. 2017Learning to Compare: Relation Network for Few-Shot Learningcited by 1,143
  5. 2018Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learningcited by 1,102
  6. 2019Decoupling Representation and Classifier for Long-Tailed Recognitioncited by 809
  7. 2018Dynamic Few-Shot Visual Learning without Forgettingcited by 385
  8. 2017Relation Networks for Object Detectioncited by 305
  9. 2020Frustratingly Simple Few-Shot Object Detectioncited by 282
  10. 2018Few-shot Object Detection via Feature Reweightingcited by 265
  11. 2019Graph Convolutional Networks for Temporal Action Localizationcited by 208
  12. 2019Few-Shot Object Detection with Attention-RPN and Multi-Relation Detectorcited by 206
  13. 2019Meta R-CNN : Towards General Solver for Instance-level Few-shot Learningcited by 182
  14. 2018Zero-shot Recognition via Semantic Embeddings and Knowledge Graphscited by 155
  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. 2020Incremental Few-Shot Object Detectioncited by 102
  18. 2018RepMet: Representative-based metric learning for classification and one-shot object detectioncited by 85
  19. 2019One-Shot Object Detection with Co-Attention and Co-Excitationcited by 67
  20. 2021Meta Faster R-CNN: Towards Accurate Few-Shot Object Detection with Attentive Feature Alignmentcited by 57
  21. 2021Activity Graph Transformer for Temporal Action Localizationcited by 48
  22. 2018Structure Inference Net: Object Detection Using Scene-Level Context and Instance-Level Relationshipscited by 41
  23. 2021Partner-Assisted Learning for Few-Shot Image Classificationcited by 36
  24. 2020OS2D: One-Stage One-Shot Object Detection by Matching Anchor Featurescited by 35
  25. 2020Restoring Negative Information in Few-Shot Object Detectioncited by 28

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 1; 2022: 19; 2023: 18; 2024: 8; 2025: 6; 2026: 3.

55 papers.

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  1. 2023Towards Open Vocabulary Learning: A Surveycited by 166
  2. 2022Few-Shot Object Detection with Fully Cross-Transformercited by 64
  3. 2023Multi-modal Queried Object Detection in the Wildcited by 47
  4. 2024Cross-Domain Few-Shot Object Detection via Enhanced Open-Set Object Detectorcited by 43
  5. 2023Explore the Power of Synthetic Data on Few-shot Object Detectioncited by 38
  6. 2021Few-Shot Object Detection: A Comprehensive Surveycited by 29
  7. 2022Integrative Few-Shot Learning for Classification and Segmentationcited by 25
  8. 2022Where is my Wallet? Modeling Object Proposal Sets for Egocentric Visual Query Localizationcited by 24
  9. 2023Supervised Masked Knowledge Distillation for Few-Shot Transformerscited by 23
  10. 2023Few-Shot Object Detection via Variational Feature Aggregationcited by 22
  11. 2023DiGeo: Discriminative Geometry-Aware Learning for Generalized Few-Shot Object Detectioncited by 21
  12. 2023Generating Features with Increased Crop-related Diversity for Few-Shot Object Detectioncited by 21
38 more
  1. 2022FS-DETR: Few-Shot DEtection TRansformer with prompting and without re-trainingcited by 20
  2. 2022Rethinking Few-Shot Object Detection on a Multi-Domain Benchmarkcited by 17
  3. 2022CD-FSOD: A Benchmark for Cross-domain Few-shot Object Detectioncited by 17
  4. 2023Detect Everything with Few Examplescited by 14
  5. 2022Efficient Few-Shot Object Detection via Knowledge Inheritancecited by 12
  6. 2022Breaking Immutable: Information-Coupled Prototype Elaboration for Few-Shot Object Detectioncited by 12
  7. 2023Reference Twice: A Simple and Unified Baseline for Few-Shot Instance Segmentationcited by 12
  8. 2022Multi-Modal Few-Shot Object Detection with Meta-Learning-Based Cross-Modal Promptingcited by 11
  9. 2022Multi-Faceted Distillation of Base-Novel Commonality for Few-shot Object Detectioncited by 11
  10. 2022Time-rEversed diffusioN tEnsor Transformer: A new TENET of Few-Shot Object Detectioncited by 10
  11. 2024AirShot: Efficient Few-Shot Detection for Autonomous Explorationcited by 10
  12. 2025No time to train! Training-Free Reference-Based Instance Segmentationcited by 9
  13. 2022Mobile Robot Manipulation using Pure Object Detectioncited by 8
  14. 2022Recent Few-Shot Object Detection Algorithms: A Survey with Performance Comparisoncited by 8
  15. 2022Hierarchical Attention Network for Few-Shot Object Detection via Meta-Contrastive Learningcited by 6
  16. 2022Multi-Scale Relational Graph Convolutional Network for Multiple Instance Learning in Histopathology Imagescited by 6
  17. 2025Generalized Semantic Contrastive Learning via Embedding Side Information for Few-Shot Object Detectioncited by 6
  18. 2025Visual Textualization for Image Prompted Object Detectioncited by 6
  19. 2023The Art of Camouflage: Few-Shot Learning for Animal Detection and Segmentationcited by 5
  20. 2024Search and Detect: Training-Free Long Tail Object Detection via Web-Image Retrievalcited by 5
  21. 2023Meta-tuning Loss Functions and Data Augmentation for Few-shot Object Detectioncited by 4
  22. 2023ECEA: Extensible Co-Existing Attention for Few-Shot Object Detectioncited by 4
  23. 2022Few-Shot Object Detection by Knowledge Distillation Using Bag-of-Visual-Words Representationscited by 3
  24. 2022Multi-Modal Few-Shot Temporal Action Detectioncited by 3
  25. 2023Few-shot Semantic Segmentation with Support-induced Graph Convolutional Networkcited by 3
  26. 2023An Effective Crop-Paste Pipeline for Few-shot Object Detectioncited by 3
  27. 2023Few-Shot Object Detection via Synthetic Features with Optimal Transportcited by 3
  28. 2024Gradient-based Sampling for Class Imbalanced Semi-supervised Object Detectioncited by 3
  29. 2024PS-TTL: Prototype-based Soft-labels and Test-Time Learning for Few-shot Object Detectioncited by 2
  30. 2025Few-Shot Learning in Video and 3D Object Detection: A Surveycited by 2
  31. 2026FSOD-VFM: Few-Shot Object Detection with Vision Foundation Models and Graph Diffusioncited by 2
  32. 2023Dual-level Interaction for Domain Adaptive Semantic Segmentationcited by 1
  33. 2024Detect an Object At Once without Fine-tuningcited by 1
  34. 2025Few-Shot Pattern Detection via Template Matching and Regressioncited by 1
  35. 2026Rethinking Prototype-based Similarity Learning for Few-Shot Object Detectioncited by 1
  36. 2022NDGGNET-A Node Independent Gate based Graph Neural Networkscited by 0
  37. 2022Automatic Bounding Box Annotation with Small Training Data Sets for Industrial Manufacturingcited by 0
  38. 2023Re-Scoring Using Image-Language Similarity for Few-Shot Object Detectioncited by 0

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