CrowdMLP: Weakly-supervised crowd counting via multi-granularity MLP

doi:10.1016/j.patcog.2023.109830

CrowdMLP: Weakly-supervised crowd counting via multi-granularity MLP

2023-12-01

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DOI：10.1016/j.patcog.2023.109830

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Currently, state-of-the-art crowd counting algorithms rely excessively on location-level annotations, which are burdensome to acquire. When only weak supervisory signals at the count level are available, it is arduous and error-prone to regress total counts due to the lack of explicit spatial constraints. To address this issue, we propose a novel and efficient counter, CrowdMLP, which explores the modelling of global dependencies of embeddings and regresses total counts by designing a multi-granularity MLP regressor. Specifically, a locally-focused pre-trained frontend is used to extract crude feature maps with intrinsic spatial cues, preventing the model from collapsing into trivial outcomes. The crude embeddings, along with the raw crowd scenes, are tokenized at different granularity levels. Next, the multi-granularity MLP mixes tokens at the dimensions of cardinality, channel, and spatial for mining global information. We also propose an effective proxy task called Split-Counting to overcome the limited samples and the lack of spatial hints in a self-supervised manner. Extensive experiments demonstrate that CrowdMLP significantly outperforms existing weakly-supervised counting algorithms and performs better than state-of-the-art location-level supervised approaches.

Keywords:

Weakly-supervised learning

Crowd counting

Multi-granularity MLP

Self-supervised proxy task

CrowdMLP: Weakly-supervised crowd counting via multi-granularity MLP

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