首个半监督多模态人群计数基准，推动弱监督下跨模态计数研究
A Benchmark for Semi-supervised Multi-modal Crowd Counting
- 构建半监督多模态人群计数的标准化评估框架
- 在不同标注比例下验证多种基线方法性能表现
- 适用于关注弱监督与多模态融合的研究者
本文首次构建了半监督多模态人群计数的基准。为奠定该未探索任务的基础，我们首先定义了半监督多模态设置及标准化协议，明确不同标注比例下的有标签-无标签数据划分方式。随后，精心设计一系列代表性基线，涵盖现有的全监督多模态方法和半监督单模态方法。最后，在所提出的基准上系统评估各方法性能。代码与数据划分将发布于 https://github.com/HenryCilence/Semi-supervised-Multimodal-Crowd-Counting。
原文摘要 · Abstract (English)
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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