A Benchmark for Semi-supervised Multi-modal Crowd Counting

• URL: http://arxiv.org/abs/2606.03646v1
• Date: Tue, 02 Jun 2026 13:38:41 GMT
• Title: A Benchmark for Semi-supervised Multi-modal Crowd Counting
• Abstract summary: This paper constructs the first benchmark on semi-supervised multi-modal crowd counting.<n>We first formulate the semi-supervised multi-modal setting and a standardized protocol that specifies the labeled-unlabeled data partition across different labeled ratios.
• Score: 70.51330672285022
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper constructs the first benchmark on semi-supervised multi-modal crowd counting. To lay the foundation for this unexplored task, we first formulate the semi-supervised multi-modal setting and a standardized protocol that specifies the labeled-unlabeled data partition across different labeled ratios. Next, to establish solid reference points, we carefully tailor a diverse set of representative baselines, including existing fully supervised multi-modal methods and semi-supervised single-modal methods. Then, we carefully evaluate their performance under our proposed benchmark. Codes and the data partition will be released on https://github.com/HenryCilence/Semi-supervised-Multimodal-Crowd-Counting.

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