DAOT: Domain-Agnostically Aligned Optimal Transport for Domain-Adaptive
  Crowd Counting
Huilin Zhu Jingling Yuan Xian Zhong Zhengwei Yang Zheng Wang Shengfeng He
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/.