DAOT: Domain-Agnostically Aligned Optimal Transport for Domain-Adaptive Crowd Counting

• URL: http://arxiv.org/abs/2308.05311v1
• Date: Thu, 10 Aug 2023 02:59:40 GMT
• Title: DAOT: Domain-Agnostically Aligned Optimal Transport for Domain-Adaptive Crowd Counting
• Authors: Huilin Zhu, Jingling Yuan, Xian Zhong, Zhengwei Yang, Zheng Wang, and Shengfeng He
• Abstract summary: Domain adaptation is commonly employed in crowd counting to bridge the domain gaps between different datasets. Existing domain adaptation methods tend to focus on inter-dataset differences while overlooking the intra-differences within the same dataset. We propose a Domain-agnostically Aligned Optimal Transport (DAOT) strategy that aligns domain-agnostic factors between domains.
• Score: 35.83485358725357
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Domain adaptation is commonly employed in crowd counting to bridge the domain gaps between different datasets. However, existing domain adaptation methods tend to focus on inter-dataset differences while overlooking the intra-differences within the same dataset, leading to additional learning ambiguities. These domain-agnostic factors, e.g., density, surveillance perspective, and scale, can cause significant in-domain variations, and the misalignment of these factors across domains can lead to a drop in performance in cross-domain crowd counting. To address this issue, we propose a Domain-agnostically Aligned Optimal Transport (DAOT) strategy that aligns domain-agnostic factors between domains. The DAOT consists of three steps. First, individual-level differences in domain-agnostic factors are measured using structural similarity (SSIM). Second, the optimal transfer (OT) strategy is employed to smooth out these differences and find the optimal domain-to-domain misalignment, with outlier individuals removed via a virtual "dustbin" column. Third, knowledge is transferred based on the aligned domain-agnostic factors, and the model is retrained for domain adaptation to bridge the gap across domains. We conduct extensive experiments on five standard crowd-counting benchmarks and demonstrate that the proposed method has strong generalizability across diverse datasets. Our code will be available at: https://github.com/HopooLinZ/DAOT/.

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