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arXiv · 2303.09245

Cross-head Supervision for Crowd Counting with Noisy Annotations

Mingliang Dai·Zhizhong Huang·Jiaqi Gao·Hongming Shan·Junping Zhang
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Abstract

Noisy annotations such as missing annotations and location shifts often exist in crowd counting datasets due to multi-scale head sizes, high occlusion, etc. These noisy annotations severely affect the model training, especially for density map-based methods. To alleviate the negative impact of noisy annotations, we propose a novel crowd counting model with one convolution head and one transformer head, in which these two heads can supervise each other in noisy areas, called Cross-Head Supervision. The resultant model, CHS-Net, can synergize different types of inductive biases for better counting. In addition, we develop a progressive cross-head supervision learning strategy to stabilize the training process and provide more reliable supervision. Extensive experimental results on ShanghaiTech and QNRF datasets demonstrate superior performance over state-of-the-art methods. Code is available at https://github.com/RaccoonDML/CHSNet.

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Mingliang Dai, Zhizhong Huang, Jiaqi Gao, Hongming Shan, Junping Zhang. 2023-03-16. Cross-head Supervision for Crowd Counting with Noisy Annotations. https://doi.org/10.1109/icassp49357.2023.10095636

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