Semi-supervised Crowd Counting via Density Agency
This addresses crowd counting with limited labeled data, offering a domain-specific incremental improvement.
The paper tackles the problem of semi-supervised crowd counting by introducing a density agency and contrastive learning, achieving superior performance over state-of-the-art methods on four datasets.
In this paper, we propose a new agency-guided semi-supervised counting approach. First, we build a learnable auxiliary structure, namely the density agency to bring the recognized foreground regional features close to corresponding density sub-classes (agents) and push away background ones. Second, we propose a density-guided contrastive learning loss to consolidate the backbone feature extractor. Third, we build a regression head by using a transformer structure to refine the foreground features further. Finally, an efficient noise depression loss is provided to minimize the negative influence of annotation noises. Extensive experiments on four challenging crowd counting datasets demonstrate that our method achieves superior performance to the state-of-the-art semi-supervised counting methods by a large margin. Code is available.