Learning the Meta Feature Transformer for Unsupervised Person Re-Identification

Qing Li, Chuan Yan and Xiaojiang Peng ()
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Qing Li: College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518118, China
Chuan Yan: Department of Computer Science, George Mason University, Fairfax, VA 22030, USA
Xiaojiang Peng: College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518118, China

Mathematics, 2024, vol. 12, issue 12, 1-14

Abstract: Although unsupervised person re-identification (Re-ID) has drawn increasing research attention, it still faces the challenge of learning discriminative features in the absence of pairwise labels across disjoint camera views. To tackle the issue of label scarcity, researchers have delved into clustering and multilabel learning using memory dictionaries. Although effective in improving unsupervised Re-ID performance, these methods require substantial computational resources and introduce additional training complexity. To address this issue, we propose a conceptually simple yet effective and learnable module effective block, named the meta feature transformer (MFT). MFT is a streamlined, lightweight network architecture that operates without the need for complex networks or feature memory bank storage. It primarily focuses on learning interactions between sample features within small groups using a transformer mechanism in each mini-batch. It then generates a new sample feature for each group through a weighted sum. The main benefits of MFT arise from two aspects: (1) it allows for the use of numerous new samples for training, which significantly expands the feature space and enhances the network’s generalization capabilities; (2) the trainable attention weights highlight the importance of samples, enabling the network to focus on more useful or distinguishable samples. We validate our method on two popular large-scale Re-ID benchmarks, where extensive evaluations show that our MFT outperforms previous methods and significantly improves Re-ID performances.

Keywords: meta feature transformer; unsupervised Re-ID; multitask learning; group feature (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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