mlsanityData to 7 Oct 2026

Query-Guided Networks for Few-shot Fine-grained Classification and Person Search

arXiv 2209.10250 ↗First posted 21 Sep 2022cs.CVcited by 3

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Abstract (arXiv)

Few-shot fine-grained classification and person search appear as distinct tasks and literature has treated them separately. But a closer look unveils important similarities: both tasks target categories that can only be discriminated by specific object details; and the relevant models should generalize to new categories, not seen during training. We propose a novel unified Query-Guided Network (QGN) applicable to both tasks. QGN consists of a Query-guided Siamese-Squeeze-and-Excitation subnetwork which re-weights both the query and gallery features across all network layers, a Query-guided Region Proposal subnetwork for query-specific localisation, and a Query-guided Similarity subnetwork for metric learning. QGN improves on a few recent few-shot fine-grained datasets, outperforming other techniques on CUB by a large margin. QGN also performs competitively on the person search CUHK-SYSU and PRW datasets, where we perform in-depth analysis.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 29 of its 43 references that are in the map. First posted on arXiv 21 Sep 2022.

Builds on

29 of 43 references.

References · 29

  1. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
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  6. 2017Prototypical Networks for Few-shot Learningcited by 3,001
  7. 2016Matching Networks for One Shot Learningcited by 2,246
  8. 2013Fine-Grained Visual Classification of Aircraftcited by 1,701
  9. 2017Learning to Compare: Relation Network for Few-Shot Learningcited by 1,139
  10. 2019A Closer Look at Few-shot Classificationcited by 679
  11. 2020Circle Loss: A Unified Perspective of Pair Similarity Optimizationcited by 402
  12. 2019Cross Attention Network for Few-shot Classificationcited by 218
17 more
  1. 2016Joint Detection and Identification Feature Learning for Person Searchcited by 190
  2. 2016Person Re-identification in the Wildcited by 157
  3. 2019Charting the Right Manifold: Manifold Mixup for Few-shot Learningcited by 135
  4. 2019When Does Self-supervision Improve Few-shot Learning?cited by 78
  5. 2019Transductive Episodic-Wise Adaptive Metric for Few-Shot Learningcited by 67
  6. 2020Instance Credibility Inference for Few-Shot Learningcited by 57
  7. 2019Learning Context Graph for Person Searchcited by 55
  8. 2021Anchor-Free Person Searchcited by 42
  9. 2019Re-ID Driven Localization Refinement for Person Searchcited by 41
  10. 2019Query-guided End-to-End Person Searchcited by 40
  11. 2020Revisiting Pose-Normalization for Fine-Grained Few-Shot Recognitioncited by 30
  12. 2020Robust Partial Matching for Person Search in the Wildcited by 30
  13. 2021Decoupled and Memory-Reinforced Networks: Towards Effective Feature Learning for One-Step Person Searchcited by 19
  14. 2020Diverse Knowledge Distillation for End-to-End Person Searchcited by 16
  15. 2019Knowledge Distillation for End-to-End Person Searchcited by 6
  16. 2021Making Person Search Enjoy the Merits of Person Re-identificationcited by 3
  17. 2019Learning Multi-level Weight-centric Features for Few-shot Learningcited by 2

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Cited by year

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

3 papers.

  1. 2025Few-Shot Learning in Video and 3D Object Detection: A Surveycited by 2
  2. 2023Detail Reinforcement Diffusion Model: Augmentation Fine-Grained Visual Categorization in Few-Shot Conditionscited by 1
  3. 2024Swap Path Network for Robust Person Search Pre-trainingcited by 0