LEVERAGING INTRA-DOMAIN KNOWLEDGE TO STRENGTHEN CROSS-DOMAIN CROWD COUNTING

Unsupervised cross-domain counting research using synthetic datasets becomes imminent when considering the laborious labeling for supervised methods. However, the existing methods only focus on learning domain shared knowledge to narrow the gap between the source domain and target domain (inter-domain gap). Nevertheless, these methods do not consider the enormous distribution gap among the target domain data itself (intra-domain gap). In this paper, we propose a two-step domain adaptation method with multi-level feature response branches, which further uses the intra-domain knowledge to strengthen the target domain's adaptability. Specifically, we first use different feature response branches to learn inter-domain knowledge more robustly, reducing the prediction inconsistency of different scenarios. Subsequently, the trained model is used to generate pseudo-labels for the target domain. The entire model was retrained by using pseudo-labels. Various experiments on synthetic dataset GCC and three real public datasets validate our proposed method's availability with higher accuracy.