Multi-modal Crowd Counting via Modal Emulation

• URL: http://arxiv.org/abs/2407.19491v1
• Date: Sun, 28 Jul 2024 13:14:57 GMT
• Title: Multi-modal Crowd Counting via Modal Emulation
• Authors: Chenhao Wang, Xiaopeng Hong, Zhiheng Ma, Yupeng Wei, Yabin Wang, Xiaopeng Fan,
• Abstract summary: We propose a modal emulation-based two-pass multi-modal crowd-counting framework. Framework consists of two key components: a emphmulti-modal inference pass and a emphcross-modal emulation pass. Experiments on both RGB-Thermal and RGB-Depth counting datasets demonstrate its superior performance compared to previous methods.
• Score: 41.959740205234446
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-modal crowd counting is a crucial task that uses multi-modal cues to estimate the number of people in crowded scenes. To overcome the gap between different modalities, we propose a modal emulation-based two-pass multi-modal crowd-counting framework that enables efficient modal emulation, alignment, and fusion. The framework consists of two key components: a \emph{multi-modal inference} pass and a \emph{cross-modal emulation} pass. The former utilizes a hybrid cross-modal attention module to extract global and local information and achieve efficient multi-modal fusion. The latter uses attention prompting to coordinate different modalities and enhance multi-modal alignment. We also introduce a modality alignment module that uses an efficient modal consistency loss to align the outputs of the two passes and bridge the semantic gap between modalities. Extensive experiments on both RGB-Thermal and RGB-Depth counting datasets demonstrate its superior performance compared to previous methods. Code available at https://github.com/Mr-Monday/Multi-modal-Crowd-Counting-via-Modal-Emulation.

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