Mining Valuable Source Domain Instances for Privacy-Preserving Domain Adaptive Object Detection
Jian Liu, Jianqiao An, Yahong Han · 2022 IEEE International Conference on Multimedia and Expo (ICME) · 2022
Conventional Domain Adaptive Object Detection (DAOD) usually learns a well-performed detector by aligning the source/target representations in hidden space, which is un-practical in real situations because of data privacy. Recently, some methods based on pseudo-label technique have been proposed for privacy-preserving DAOD. However, they bring new difficulties in evaluating the quality of these labels. In this paper, we propose a Mining Valuable source domain instances (MiVi) algorithm for privacy-preserving DAOD where data from different domains cannot be aggregated. Specifically, we explore a novel Instances Refinement strategy to mine valuable instance-level samples from the source data, which are close to the feature distribution of the target domain. To achieve this, we are the first to introduce self-supervised learning to guide the detector to learn the instance-level features in different domains. Then, by reserving labeled source domain instance-level samples with domain-invariant features and discarding the ones with source domain-specific features, we transform unsupervised DAOD into supervised object detection. Extensive experiments in four representative DAOD scenarios show that our method outperforms existing methods.