Abstract
Semi-supervised crowd counting is crucial for addressing the high annotation costs of densely populated scenes. Although several methods based on pseudo-labeling have been proposed, it remains challenging to effectively and accurately utilize unlabeled data. In this paper, we propose a novel framework called Taste More Taste Better (TMTB), which emphasizes both data and model aspects. Firstly, we explore a data augmentation technique well-suited for the crowd counting task. By inpainting the background regions, this technique can effectively enhance data diversity while preserving the fidelity of the entire scenes. Secondly, we introduce the Visual State Space Model as backbone to capture the global context information from crowd scenes, which is crucial for extremely crowded, low-light, and adverse weather scenarios. In addition to the traditional regression head for exact prediction, we employ an Anti-Noise classification head to provide less exact but more accurate supervision, since the regression head is sensitive to noise in manual annotations. We conduct extensive experiments on four benchmark datasets and show that our method outperforms state-of-the-art methods by a large margin. Code is publicly available on this https URL.
Abstract (translated)
半监督人群计数对于解决密集场景中的高标注成本问题至关重要。尽管已经提出了几种基于伪标签的方法，但如何有效地利用未标记数据仍是一个挑战。在本文中，我们提出了一种新颖的框架——“尝得更多，变得更好”（Taste More Taste Better, TMTB），该框架同时强调了数据和模型方面的重要性。 首先，我们探索了一种适合人群计数任务的数据增强技术。通过修复背景区域，这种技术可以有效增加数据多样性的同时保持整个场景的真实感。 其次，我们引入视觉状态空间模型作为骨干网络来捕捉从人群中获取的全局上下文信息，这对于极其拥挤、光线不足和恶劣天气场景尤为重要。除了传统的回归头用于精确预测之外，我们还采用了一种抗噪分类头提供不太精确但更准确的监督，因为回归头对人工标注中的噪声非常敏感。 我们在四个基准数据集上进行了广泛的实验，并显示我们的方法在很大程度上优于最先进的方法。代码可在[此处](https://this https URL)公开获取（请注意，实际链接应替换为有效的公共访问地址）。
URL
https://arxiv.org/abs/2503.17984
