Crowd Counting with Sparse Annotation

• URL: http://arxiv.org/abs/2304.06021v1
• Date: Wed, 12 Apr 2023 17:57:48 GMT
• Title: Crowd Counting with Sparse Annotation
• Authors: Shiwei Zhang, Zhengzheng Wang, Qing Liu, Fei Wang, Wei Ke, Tong Zhang
• Abstract summary: We argue that sparse labeling can reduce redundancy of full annotation and capture more diverse information from distant individuals. We propose a point-based Progressive Point Matching network (PPM) to explore the crowd from the whole image with sparse annotation. Our experimental results show that PPM outperforms previous semi-supervised crowd counting methods with the same amount of annotation by a large margin.
• Score: 28.793141115957564
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents a new annotation method called Sparse Annotation (SA) for crowd counting, which reduces human labeling efforts by sparsely labeling individuals in an image. We argue that sparse labeling can reduce the redundancy of full annotation and capture more diverse information from distant individuals that is not fully captured by Partial Annotation methods. Besides, we propose a point-based Progressive Point Matching network (PPM) to better explore the crowd from the whole image with sparse annotation, which includes a Proposal Matching Network (PMN) and a Performance Restoration Network (PRN). The PMN generates pseudo-point samples using a basic point classifier, while the PRN refines the point classifier with the pseudo points to maximize performance. Our experimental results show that PPM outperforms previous semi-supervised crowd counting methods with the same amount of annotation by a large margin and achieves competitive performance with state-of-the-art fully-supervised methods.

Related papers

• From Few to More: Scribble-based Medical Image Segmentation via Masked Context Modeling and Continuous Pseudo Labels [17.433808197776003]
  We propose a weakly supervised framework designed for medical image segmentation. MaCo employs masked context modeling and continuous pseudo labels. We evaluate MaCo against other weakly supervised methods using three public datasets.
  arXiv  Detail & Related papers  (2024-08-23T03:19:20Z)
• A Point-Neighborhood Learning Framework for Nasal Endoscope Image Segmentation [43.0260204534598]
  We propose a weakly semi-supervised method called Point-Neighborhood Learning (PNL) framework. To mine the prior of the pixels surrounding the annotated point, we transform a single-point annotation into a circular area named a point-neighborhood. Our method greatly improves performance without changing the structure of segmentation network.
  arXiv  Detail & Related papers  (2024-05-30T13:25:25Z)
• Focus for Free in Density-Based Counting [56.961229110268036]
  We introduce two methods that repurpose the available point annotations to enhance counting performance. The first is a counting-specific augmentation that leverages point annotations to simulate occluded objects in both input and density images. The second method, foreground distillation, generates foreground masks from the point annotations, from which we train an auxiliary network on images with blacked-out backgrounds.
  arXiv  Detail & Related papers  (2023-06-08T11:54:37Z)
• Multi-View Knowledge Distillation from Crowd Annotations for Out-of-Domain Generalization [53.24606510691877]
  We propose new methods for acquiring soft-labels from crowd-annotations by aggregating the distributions produced by existing methods. We demonstrate that these aggregation methods lead to the most consistent performance across four NLP tasks on out-of-domain test sets.
  arXiv  Detail & Related papers  (2022-12-19T12:40:18Z)
• Neighbour Consistency Guided Pseudo-Label Refinement for Unsupervised Person Re-Identification [80.98291772215154]
  Unsupervised person re-identification (ReID) aims at learning discriminative identity features for person retrieval without any annotations. Recent advances accomplish this task by leveraging clustering-based pseudo labels. We propose a Neighbour Consistency guided Pseudo Label Refinement framework.
  arXiv  Detail & Related papers  (2022-11-30T09:39:57Z)
• Point-Teaching: Weakly Semi-Supervised Object Detection with Point Annotations [81.02347863372364]
  We present Point-Teaching, a weakly semi-supervised object detection framework. Specifically, we propose a Hungarian-based point matching method to generate pseudo labels for point annotated images. We propose a simple-yet-effective data augmentation, termed point-guided copy-paste, to reduce the impact of the unmatched points.
  arXiv  Detail & Related papers  (2022-06-01T07:04:38Z)
• Object Localization under Single Coarse Point Supervision [107.46800858130658]
  We propose a POL method using coarse point annotations, relaxing the supervision signals from accurate key points to freely spotted points. CPR constructs point bags, selects semantic-correlated points, and produces semantic center points through multiple instance learning (MIL) In this way, CPR defines a weakly supervised evolution procedure, which ensures training high-performance object localizer under coarse point supervision.
  arXiv  Detail & Related papers  (2022-03-17T14:14:11Z)
• Rethinking Counting and Localization in Crowds:A Purely Point-Based Framework [59.578339075658995]
  We propose a purely point-based framework for joint crowd counting and individual localization. We design an intuitive solution under this framework, which is called Point to Point Network (P2PNet)
  arXiv  Detail & Related papers  (2021-07-27T11:41:50Z)
• Learning from Crowds with Sparse and Imbalanced Annotations [29.596070201105274]
  crowdsourcing has established itself as an efficient labeling solution through resorting to non-expert crowds. One common practice is to distribute each instance to multiple workers, whereas each worker only annotates a subset of data, resulting in the it sparse annotation phenomenon. We propose one self-training based approach named it Self-Crowd by progressively adding confident pseudo-annotations and rebalancing the annotation distribution.
  arXiv  Detail & Related papers  (2021-07-11T13:06:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.