Heterogeneous Pseudo-Supervised Learning for Few-shot Person Re-Identification. (October 2022)

• Record Type: Journal Article
• Authors: Zhao, Jing; Lan, Long; Huang, Da; Ren, Jing; Yang, Wenjing
• Is Part Of: Neural networks. Volume 154(2022)
• Page Start: 521
• Page End: 537
• Publication Date: 2022-10
• Subjects: Pseudo-supervised learning -- Heterogeneous pseudo-supervised learning -- Knowledge fusion -- Few-shot -- Person re-identification; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2022.06.017
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: How to obtain good retrieval performance in the case of few-shot labeled samples is the current research focus of Person Re-Identification. To facilitate formal analysis, we formally put forward the concept of Pseudo-Supervised Learning (PSL) to represent a series of research works based on label generation under few-shot condition. Through extensive investigations, we find that the main problem that needs to be solved of PSL is how we can improve the quality of pseudo-label. To solve this problem, in this work, we proposed a simple yet effective Heterogeneous Pseudo-Supervised Learning (H-PSL) framework based on classical PSL to implement asynchronous match, which boosts the feature expression and then a better label prediction in the following. Specifically, a novel isomer is constructed as the feature extractor and is trained with a much larger amount of pseudo-supervised data, i.e., samples with pseudo-labels. In this way, the isomer obtains advanced feature expression. We then deliberately implement a cross-level asynchronous match mechanism between model and pseudo-supervised data. As a result, the quality of pseudo-label is greatly improved and the feature expression performance also be optimized accordingly. In addition, to make better use of pseudo-supervised data, we also designed a knowledge fusion strategy to integrate the pseudo labels and their confidence which are easily obtained by the base model and isomer. Encouragingly, knowledge fusion strategy further removes the noise-labeled samples from candidate data. We conduct experiments on four popular datasets to fully verify the universality of the proposed method. The experimental results show that the proposed method improves the performance of all compared baseline works.