mlsanityData to 13 Oct 2026

Calibrating Uncertainty for Semi-Supervised Crowd Counting

arXiv 2308.09887 ↗First posted 19 Aug 2023cs.CV cs.LGcited by 22

Not on the map. Why?

Abstract (arXiv)

Semi-supervised crowd counting is an important yet challenging task. A popular approach is to iteratively generate pseudo-labels for unlabeled data and add them to the training set. The key is to use uncertainty to select reliable pseudo-labels. In this paper, we propose a novel method to calibrate model uncertainty for crowd counting. Our method takes a supervised uncertainty estimation strategy to train the model through a surrogate function. This ensures the uncertainty is well controlled throughout the training. We propose a matching-based patch-wise surrogate function to better approximate uncertainty for crowd counting tasks. The proposed method pays a sufficient amount of attention to details, while maintaining a proper granularity. Altogether our method is able to generate reliable uncertainty estimation, high quality pseudolabels, and achieve state-of-the-art performance in semisupervised crowd counting.

Key facts. Cited by 22 papers in the map (data through 13 Oct 2026). Builds on 49 of its 65 references that are in the map. First posted on arXiv 19 Aug 2023.

Builds on

49 of 65 references.

References · 49

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,093
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,321
  3. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,340
  4. 2020FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidencecited by 2,082
  5. 2019Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentationcited by 378
  6. 2021End-to-End Semi-Supervised Object Detection with Soft Teachercited by 315
  7. 2018A Probabilistic U-Net for Segmentation of Ambiguous Imagescited by 281
  8. 2018CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenescited by 267
  9. 2018Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowdscited by 186
  10. 2019Bayesian Loss for Crowd Count Estimation with Point Supervisioncited by 162
  11. 2019Confidence Calibration and Predictive Uncertainty Estimation for Deep Medical Image Segmentationcited by 162
  12. 2021Geometry Uncertainty Projection Network for Monocular 3D Object Detectioncited by 155
37 more
  1. 2021Rethinking Counting and Localization in Crowds:A Purely Point-Based Frameworkcited by 152
  2. 2020NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and Localizationcited by 151
  3. 2018Context-Aware Crowd Countingcited by 150
  4. 2019Learning from Synthetic Data for Crowd Counting in the Wildcited by 148
  5. 2018Bounding Box Regression with Uncertainty for Accurate Object Detectioncited by 143
  6. 2020Distribution Matching for Crowd Countingcited by 143
  7. 2015End-to-end people detection in crowded scenescited by 140
  8. 2019PHiSeg: Capturing Uncertainty in Medical Image Segmentationcited by 128
  9. 2020Confidence-Aware Learning for Deep Neural Networkscited by 113
  10. 2018Bias-Reduced Uncertainty Estimation for Deep Neural Classifierscited by 109
  11. 2020JHU-CROWD++: Large-Scale Crowd Counting Dataset and A Benchmark Methodcited by 109
  12. 2019Locate, Size and Count: Accurately Resolving People in Dense Crowds via Detectioncited by 73
  13. 2020Localization in the Crowd with Topological Constraintscited by 68
  14. 2019Point in, Box out: Beyond Counting Persons in Crowdscited by 62
  15. 2018Leveraging Unlabeled Data for Crowd Counting by Learning to Rankcited by 57
  16. 2019Crowd Counting and Density Estimation by Trellis Encoder-Decoder Networkcited by 56
  17. 2018Where are the Blobs: Counting by Localization with Point Supervisioncited by 54
  18. 2019Exploiting Unlabeled Data in CNNs by Self-supervised Learning to Rankcited by 52
  19. 2018Revisiting Perspective Information for Efficient Crowd Countingcited by 49
  20. 2018Iterative Crowd Countingcited by 48
  21. 2019From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquercited by 48
  22. 2018ADCrowdNet: An Attention-injective Deformable Convolutional Network for Crowd Understandingcited by 45
  23. 2021Spatial Uncertainty-Aware Semi-Supervised Crowd Countingcited by 38
  24. 2019Multi-Level Bottom-Top and Top-Bottom Feature Fusion for Crowd Countingcited by 36
  25. 2020NAS-Count: Counting-by-Density with Neural Architecture Searchcited by 36
  26. 2020Semi-Supervised Crowd Counting via Self-Training on Surrogate Taskscited by 35
  27. 2019Pushing the Frontiers of Unconstrained Crowd Counting: New Dataset and Benchmark Methodcited by 32
  28. 2023Topology-Aware Uncertainty for Image Segmentationcited by 32
  29. 2020Weighing Counts: Sequential Crowd Counting by Reinforcement Learningcited by 30
  30. 2020Adaptive Mixture Regression Network with Local Counting Map for Crowd Countingcited by 28
  31. 2020Shallow Feature Based Dense Attention Network for Crowd Countingcited by 28
  32. 2020Learning to Count in the Crowd from Limited Labeled Datacited by 27
  33. 2020Pixel-wise Crowd Understanding via Synthetic Datacited by 23
  34. 2020Active Crowd Counting with Limited Supervisioncited by 20
  35. 2022Learning Probabilistic Topological Representations Using Discrete Morse Theorycited by 20
  36. 2022Semi-supervised Crowd Counting via Density Agencycited by 18
  37. 2023Confidence Estimation Using Unlabeled Datacited by 8

Built on by

Cited by year (2026 to 13 Oct)

Citing papers per year: 2023: 2; 2024: 5; 2025: 11; 2026: 4.

22 papers.

Sort:

By area:

  1. 2024OmniCount: Multi-label Object Counting with Semantic-Geometric Priorscited by 16
  2. 2025Uncertainty Quantification for Machine Learning in Healthcare: A Surveycited by 13
  3. 2023Semi-supervised Segmentation of Histopathology Images with Noise-Aware Topological Consistencycited by 11
  4. 2023Semi-Supervised Crowd Counting with Contextual Modeling: Facilitating Holistic Understanding of Crowd Scenescited by 6
  5. 2025Point-to-Region Loss for Semi-Supervised Point-Based Crowd Countingcited by 6
  6. 2024Spatial Diffusion for Cell Layout Generationcited by 5
  7. 2025Taste More, Taste Better: Diverse Data and Strong Model Boost Semi-Supervised Crowd Countingcited by 5
  8. 2024Robust Zero-Shot Crowd Counting and Localization With Adaptive Resolution SAMcited by 4
  9. 2024Diffusion-Refined VQA Annotations for Semi-Supervised Gaze Followingcited by 4
  10. 2025MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentationcited by 3
  11. 2025Text-promptable Object Counting via Quantity Awareness Enhancementcited by 2
  12. 2025Semi-Supervised Multi-View Crowd Counting by Ranking Multi-View Fusion Modelscited by 2
10 more
  1. 2026Bootstrapping MLLM for Weakly-Supervised Class-Agnostic Object Countingcited by 2
  2. 2024Enhancing Zero-shot Counting via Language-guided Exemplar Learningcited by 1
  3. 2025Consistent-Point: Consistent Pseudo-Points for Semi-Supervised Crowd Counting and Localizationcited by 1
  4. 2025Exploring Scale Shift in Crowd Localization under the Context of Domain Generalizationcited by 1
  5. 2026AdaCount: Training-Free Similarity-Guided Spatial and Feature Adaptation for Zero-Shot Object Countingcited by 1
  6. 2025Count2Density: Crowd Density Estimation without Location-level Annotationscited by 0
  7. 2025Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDAcited by 0
  8. 2025Hazy Pedestrian Trajectory Prediction via Physical Priors and Graph-Mambacited by 0
  9. 2026Exclusivity-Guided Mask Learning for Semi-Supervised Crowd Instance Segmentation and Countingcited by 0
  10. 2026ABACUS: Adapting Unified Foundation Model for Bridging Image Count Understanding and Generationcited by 0