Weakly Supervised Cross-domain Person Re-Identification Algorithm based on Small Sample Learning

Person Re-identification (Re-ID) algorithms based on deep learning are developing rapidly, and supervised learning methods ensure a steady improvement in algorithm performance by virtue of massive amounts of data. However, in the real environment, data collection and labeling of the current scene is time-consuming and laborious, and the actual performance of the algorithmic model is often poor. Therefore, the design of the model should focus on extracting and abstracting the information contained in the data under limited conditions. In this paper, we focus on the problems of strong data since, weak cross-domain capability and low accuracy encountered in Re-ID in weakly supervised scenarios. First, we implement a joint training framework with the help of small sample learning and cross-domain migration for Re-ID. Second, an residual compensation and fusion attention (RCFA) module is designed with the help of residual compensation and fused attention module, based on which a model base framework is built to explore the impact brought by different insertion positions. Third, to solve the problem of low accuracy caused by insufficient data coverage of small samples, a fusion of shallow features and deep features is designed to enable the model to weighted fusion of shallow detail information and deep semantic information. Finally, by selecting different camera images in Market1501 dataset and DukeMTMC-reID dataset as small samples respectively, and introducing another dataset data for joint training, we demonstrate the feasibility of this joint training framework, which can perform weakly supervised cross-domain Re-ID based on small sample data.