mlsanityData to 7 Oct 2026

Consistency-Aware Anchor Pyramid Network for Crowd Localization

arXiv 2212.04067 ↗First posted 8 Dec 2022cs.CVcited by 8

Not on the map. Why?

Abstract (arXiv)

Crowd localization aims to predict the spatial position of humans in a crowd scenario. We observe that the performance of existing methods is challenged from two aspects: (i) ranking inconsistency between test and training phases; and (ii) fixed anchor resolution may underfit or overfit crowd densities of local regions. To address these problems, we design a supervision target reassignment strategy for training to reduce ranking inconsistency and propose an anchor pyramid scheme to adaptively determine the anchor density in each image region. Extensive experimental results on three widely adopted datasets (ShanghaiTech A&B, JHU-CROWD++, UCF-QNRF) demonstrate the favorable performance against several state-of-the-art methods.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 46 of its 68 references that are in the map. First posted on arXiv 8 Dec 2022.

Builds on

46 of 68 references.

References · 46

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34 more
  1. 2019Deep High-Resolution Representation Learning for Visual Recognitioncited by 1,417
  2. 2019Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selectioncited by 539
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  6. 2018Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowdscited by 186
  7. 2019Bayesian Loss for Crowd Count Estimation with Point Supervisioncited by 162
  8. 2021Rethinking Counting and Localization in Crowds:A Purely Point-Based Frameworkcited by 152
  9. 2020NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and Localizationcited by 151
  10. 2020Probabilistic Anchor Assignment with IoU Prediction for Object Detectioncited by 150
  11. 2020Distribution Matching for Crowd Countingcited by 143
  12. 2020JHU-CROWD++: Large-Scale Crowd Counting Dataset and A Benchmark Methodcited by 109
  13. 2022An End-to-End Transformer Model for Crowd Localizationcited by 74
  14. 2019Locate, Size and Count: Accurately Resolving People in Dense Crowds via Detectioncited by 73
  15. 2016Finding Tiny Facescited by 72
  16. 2020End-to-End Object Detection with Fully Convolutional Networkcited by 70
  17. 2020Localization in the Crowd with Topological Constraintscited by 68
  18. 2019Point in, Box out: Beyond Counting Persons in Crowdscited by 62
  19. 2020Detection in Crowded Scenes: One Proposal, Multiple Predictionscited by 59
  20. 2018Where are the Blobs: Counting by Localization with Point Supervisioncited by 54
  21. 2020What Makes for End-to-End Object Detection?cited by 47
  22. 2019Multi-Level Bottom-Top and Top-Bottom Feature Fusion for Crowd Countingcited by 36
  23. 2020NAS-Count: Counting-by-Density with Neural Architecture Searchcited by 36
  24. 2020A Self-Training Approach for Point-Supervised Object Detection and Counting in Crowdscited by 36
  25. 2021Focal Inverse Distance Transform Maps for Crowd Localizationcited by 36
  26. 2019AutoScale: Learning to Scale for Crowd Counting and Localizationcited by 35
  27. 2018Locating Objects Without Bounding Boxescited by 25
  28. 2020Learning Independent Instance Maps for Crowd Localizationcited by 24
  29. 2022Crowd Localization from Gaussian Mixture Scoped Knowledge and Scoped Teachercited by 12
  30. 2020Counting People by Estimating People Flowscited by 11
  31. 2021Congested Crowd Instance Localization with Dilated Convolutional Swin Transformercited by 11
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  33. 2022CrowdFormer: Weakly-supervised Crowd counting with Improved Generalizabilitycited by 8
  34. 2021LDC-Net: A Unified Framework for Localization, Detection and Counting in Dense Crowdscited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 5; 2026: 2.

8 papers.

  1. 2025SoftHGNN: Soft Hypergraph Neural Networks for General Visual Recognitioncited by 4
  2. 2024Dynamic Proxy Domain Generalizes the Crowd Localization by Better Binary Segmentationcited by 2
  3. 2025Learning Extremely High Density Crowds as Active Matterscited by 1
  4. 2025Consistent-Point: Consistent Pseudo-Points for Semi-Supervised Crowd Counting and Localizationcited by 1
  5. 2025Constrained Gaussian Process Motion Planning via Stein Variational Newton Inferencecited by 0
  6. 2025DSGC-Net: A Dual-Stream Graph Convolutional Network for Crowd Counting via Feature Correlation Miningcited by 0
  7. 2026Count Anythingcited by 0
  8. 2026Depth-Guided Video Object Counting in Crowded Scenescited by 0