CrowdMLP: Weakly-Supervised Crowd Counting via Multi-Granularity MLP

• URL: http://arxiv.org/abs/2203.08219v1
• Date: Tue, 15 Mar 2022 19:57:55 GMT
• Title: CrowdMLP: Weakly-Supervised Crowd Counting via Multi-Granularity MLP
• Authors: Mingjie Wang, Jun Zhou, Hao Cai, Minglun Gong
• Abstract summary: Existing state-of-the-art crowd counting algorithms rely excessively on location-level annotations. CrowdMLP is presented, which probes into modelling global dependencies of embeddings and regressing total counts. Experiments demonstrate that CrowdMLP significantly outperforms existing weakly-supervised counting algorithms.
• Score: 19.42355176075503
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Existing state-of-the-art crowd counting algorithms rely excessively on location-level annotations, which are burdensome to acquire. When only count-level (weak) supervisory signals are available, it is arduous and error-prone to regress total counts due to the lack of explicit spatial constraints. To address this issue, a novel and efficient counter (referred to as CrowdMLP) is presented, which probes into modelling global dependencies of embeddings and regressing total counts by devising a multi-granularity MLP regressor. In specific, a locally-focused pre-trained frontend is cascaded to extract crude feature maps with intrinsic spatial cues, which prevent the model from collapsing into trivial outcomes. The crude embeddings, along with raw crowd scenes, are tokenized at different granularity levels. The multi-granularity MLP then proceeds to mix tokens at the dimensions of cardinality, channel, and spatial for mining global information. An effective proxy task, namely Split-Counting, is also proposed to evade the barrier of limited samples and the shortage of spatial hints in a self-supervised manner. Extensive experiments demonstrate that CrowdMLP significantly outperforms existing weakly-supervised counting algorithms and performs on par with state-of-the-art location-level supervised approaches.

Related papers

• Robust Zero-Shot Crowd Counting and Localization With Adaptive Resolution SAM [55.93697196726016]
  We propose a simple yet effective crowd counting method by utilizing the Segment-Everything-Everywhere Model (SEEM) We show that SEEM's performance in dense crowd scenes is limited, primarily due to the omission of many persons in high-density areas. Our proposed method achieves the best unsupervised performance in crowd counting, while also being comparable to some supervised methods.
  arXiv  Detail & Related papers  (2024-02-27T13:55:17Z)
• Combating Representation Learning Disparity with Geometric Harmonization [50.29859682439571]
  We propose a novel Geometric Harmonization (GH) method to encourage category-level uniformity in representation learning. Our proposal does not alter the setting of SSL and can be easily integrated into existing methods in a low-cost manner.
  arXiv  Detail & Related papers  (2023-10-26T17:41:11Z)
• Rethinking Clustering-Based Pseudo-Labeling for Unsupervised Meta-Learning [146.11600461034746]
  Method for unsupervised meta-learning, CACTUs, is a clustering-based approach with pseudo-labeling. This approach is model-agnostic and can be combined with supervised algorithms to learn from unlabeled data. We prove that the core reason for this is lack of a clustering-friendly property in the embedding space.
  arXiv  Detail & Related papers  (2022-09-27T19:04:36Z)
• Reinforcing Local Feature Representation for Weakly-Supervised Dense Crowd Counting [21.26385035473938]
  We propose a self-adaptive feature similarity learning network and a global-local consistency loss to reinforce local representation. Our proposed method based on different backbones narrows the gap between weakly-supervised and fully-supervised dense crowd counting.
  arXiv  Detail & Related papers  (2022-02-22T05:53:51Z)
• Optimal Clustering with Bandit Feedback [57.672609011609886]
  This paper considers the problem of online clustering with bandit feedback. It includes a novel stopping rule for sequential testing that circumvents the need to solve any NP-hard weighted clustering problem as its subroutines. We show through extensive simulations on synthetic and real-world datasets that BOC's performance matches the lower boundally, and significantly outperforms a non-adaptive baseline algorithm.
  arXiv  Detail & Related papers  (2022-02-09T06:05:05Z)
• Lattice-Based Methods Surpass Sum-of-Squares in Clustering [98.46302040220395]
  Clustering is a fundamental primitive in unsupervised learning. Recent work has established lower bounds against the class of low-degree methods. We show that, perhaps surprisingly, this particular clustering model textitdoes not exhibit a statistical-to-computational gap.
  arXiv  Detail & Related papers  (2021-12-07T18:50:17Z)
• 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)
• PSCNet: Pyramidal Scale and Global Context Guided Network for Crowd Counting [44.306790250158954]
  This paper proposes a novel crowd counting approach based on pyramidal scale module (PSM) and global context module (GCM) PSM is used to adaptively capture multi-scale information, which can identify a fine boundary of crowds with different image scales. GCM is devised with low-complexity and lightweight manner, to make the interactive information across the channels of the feature maps more efficient.
  arXiv  Detail & Related papers  (2020-12-07T11:35:56Z)
• 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)

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.