Cross-supervised Crowd Counting via Multi-scale Channel Attention.

Due to the challenges posed by large-scale variability in crowd images and overlapping and occlusion of people in high-density regions, traditional CNNs with fixed-size convolution kernels or transformers lacking 2D locality and channel adaptation need to struggle to cope with this challenge. While Transformers have a global receptive field for long sequence tasks, CNNs exhibit better generalization and 2D locality. In order to combine the advantages of both approaches, this paper proposes a dual-branch multi-scale attention network (DBMSA- Net). First of all, we propose a multi-scale channel attention convolution module to extract features at different scales while enhancing channel adaptation. Furtherly, local features are augmented using a feed-forward neural network that is more suitable for visual tasks. Then an efficient lightweight multi-scale regression head is employed to predict density maps. Finally, progressive cross-head supervision is introduced as a loss function to dynamically supervise instance labels noise and mitigate its effect. Extensive experiments are conducted on three crowd counting datasets (ShanghaiTech Part A, ShanghaiTech Part B, UCF-QNRF) to validate the effectiveness of the proposed method and the results show that DBMSA-Net outperforms state-of-the-art methods. [ABSTRACT FROM AUTHOR]