Semi-Supervised Crowd Counting from Unlabeled Data

• URL: http://arxiv.org/abs/2108.13969v3
• Date: Tue, 26 Mar 2024 16:13:26 GMT
• Title: Semi-Supervised Crowd Counting from Unlabeled Data
• Authors: Haoran Duan, Fan Wan, Rui Sun, Zeyu Wang, Varun Ojha, Yu Guan, Hubert P. H. Shum, Bingzhang Hu, Yang Long,
• Abstract summary: Crowd behavior analysis can be applied to help the daily transportation statistics and planning, which helps the smart city construction. Recent works achieved promising performance but relied on the supervised paradigm with expensive crowd annotations. We proposed a semi-supervised learning framework $S4textitCrowd$, which can leverage both unlabeled/labeled data for robust crowd counting.
• Score: 19.990275518280427
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automatic Crowd behavior analysis can be applied to effectively help the daily transportation statistics and planning, which helps the smart city construction. As one of the most important keys, crowd counting has drawn increasing attention. Recent works achieved promising performance but relied on the supervised paradigm with expensive crowd annotations. To alleviate the annotation cost in real-world transportation scenarios, in this work we proposed a semi-supervised learning framework $S^{4}\textit{Crowd}$, which can leverage both unlabeled/labeled data for robust crowd counting. In the unsupervised pathway, two \textit{self-supervised losses} were proposed to simulate the crowd variations such as scale, illumination, based on which supervised information pseudo labels were generated and gradually refined. We also proposed a crowd-driven recurrent unit \textit{Gated-Crowd-Recurrent-Unit (GCRU)}, which can preserve discriminant crowd information by extracting second-order statistics, yielding pseudo labels with improved quality. A joint loss including both unsupervised/supervised information was proposed, and a dynamic weighting strategy was employed to balance the importance of the unsupervised loss and supervised loss at different training stages. We conducted extensive experiments on four popular crowd counting datasets in semi-supervised settings. Experimental results supported the effectiveness of each proposed component in our $S^{4}$Crowd framework. Our method achieved competitive performance in semi-supervised learning approaches on these crowd counting datasets.

Related papers

• Scaling Up Semi-supervised Learning with Unconstrained Unlabelled Data [27.75143621836449]
  We propose UnMixMatch, a semi-supervised learning framework which can learn effective representations from unconstrained data. We perform extensive experiments on 4 commonly used datasets and demonstrate superior performance over existing semi-supervised methods with a performance boost of 4.79%.
  arXiv  Detail & Related papers  (2023-06-02T01:07:14Z)
• Semi-supervised Crowd Counting via Density Agency [57.3635501421658]
  We build a learnable auxiliary structure, namely the density agency to bring the recognized foreground regional features close to corresponding density sub-classes. Second, we propose a density-guided contrastive learning loss to consolidate the backbone feature extractor. Third, we build a regression head by using a transformer structure to refine the foreground features further.
  arXiv  Detail & Related papers  (2022-09-07T06:34:00Z)
• Wisdom of (Binned) Crowds: A Bayesian Stratification Paradigm for Crowd Counting [16.09823718637455]
  We analyze the performance of crowd counting approaches across standard datasets at per strata level and in aggregate. Our contributions represent a nuanced, statistically balanced and fine-grained characterization of performance for crowd counting approaches.
  arXiv  Detail & Related papers  (2021-08-19T16:50:31Z)
• Improve Unsupervised Pretraining for Few-label Transfer [80.58625921631506]
  In this paper, we find this conclusion may not hold when the target dataset has very few labeled samples for finetuning. We propose a new progressive few-label transfer algorithm for real applications.
  arXiv  Detail & Related papers  (2021-07-26T17:59:56Z)
• WSSOD: A New Pipeline for Weakly- and Semi-Supervised Object Detection [75.80075054706079]
  We propose a weakly- and semi-supervised object detection framework (WSSOD) An agent detector is first trained on a joint dataset and then used to predict pseudo bounding boxes on weakly-annotated images. The proposed framework demonstrates remarkable performance on PASCAL-VOC and MSCOCO benchmark, achieving a high performance comparable to those obtained in fully-supervised settings.
  arXiv  Detail & Related papers  (2021-05-21T11:58:50Z)
• Bayesian Semi-supervised Crowdsourcing [71.20185379303479]
  Crowdsourcing has emerged as a powerful paradigm for efficiently labeling large datasets and performing various learning tasks. This work deals with semi-supervised crowdsourced classification, under two regimes of semi-supervision.
  arXiv  Detail & Related papers  (2020-12-20T23:18:51Z)
• Semi-Supervised Crowd Counting via Self-Training on Surrogate Tasks [50.78037828213118]
  This paper tackles the semi-supervised crowd counting problem from the perspective of feature learning. We propose a novel semi-supervised crowd counting method which is built upon two innovative components.
  arXiv  Detail & Related papers  (2020-07-07T05:30:53Z)
• Learning to Count in the Crowd from Limited Labeled Data [109.2954525909007]
  We focus on reducing the annotation efforts by learning to count in the crowd from limited number of labeled samples. Specifically, we propose a Gaussian Process-based iterative learning mechanism that involves estimation of pseudo-ground truth for the unlabeled data.
  arXiv  Detail & Related papers  (2020-07-07T04:17:01Z)

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.