Semi-supervised Crowd Counting via Density Agency

• URL: http://arxiv.org/abs/2209.02955v1
• Date: Wed, 7 Sep 2022 06:34:00 GMT
• Title: Semi-supervised Crowd Counting via Density Agency
• Authors: Hui Lin and Zhiheng Ma and Xiaopeng Hong and Yaowei Wang and Zhou Su
• Abstract summary: We build a learnable auxiliary structure, namely the density agency to bring the recognized foreground regional features close to corresponding density sub-classes. 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.
• Score: 57.3635501421658
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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