Source-Free Unsupervised Cross-Domain Pedestrian Detection via Pseudo Label Mining and Screening
Zhi‐Ri Tang, Qianfen Jiao, Jian Zhong, Si Wu, Hau−San Wong · 2022 IEEE International Conference on Multimedia and Expo (ICME) · 2022
Although current cross-domain pedestrian detection frame-works have obtained certain positive results, the performance is still source data dependent, which is cumbersome and im-practical in practical applications. To address this issue, we propose a source-free unsupervised pedestrian detection with pseudo label mining and screening. First, a modified CSP de-tector with DropBlock and three detection heads is presented. Then, a multi-expert method is proposed to fuse pseudo la-bels from three detection heads. Finally, a clustering-based self-supervised learning is adopted to categorize pseudo la-bels into positive and negative classes, which forms a set of clusters via similarity of pseudo labels and give classification results based on two confidence scores of each label from the detector backbone and multi-expert fusion. Experimental re-sults on three benchmark datasets show that the proposed approach can achieve state-of-the-art performance and be even comparable with other latest works using source data.