Domain Consistency Representation Learning for Lifelong Person Re-Identification

• URL: http://arxiv.org/abs/2409.19954v2
• Date: Tue, 19 Nov 2024 08:17:30 GMT
• Title: Domain Consistency Representation Learning for Lifelong Person Re-Identification
• Authors: Shiben Liu, Qiang Wang, Huijie Fan, Weihong Ren, Baojie Fan, Yandong Tang,
• Abstract summary: Lifelong person re-identification (LReID) exhibits a contradictory relationship between intra-domain discrimination and inter-domain gaps when learning from continuous data. We propose a novel domain consistency representation learning (DCR) model that explores global and attribute-wise representations to balance intra-domain discrimination and inter-domain gaps. Our model achieves superior performance compared to state-of-the-art LReID methods.
• Score: 13.02156660844767
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Lifelong person re-identification (LReID) exhibits a contradictory relationship between intra-domain discrimination and inter-domain gaps when learning from continuous data. Intra-domain discrimination focuses on individual nuances (e.g. clothing type, accessories, etc.), while inter-domain gaps emphasize domain consistency. Achieving a trade-off between maximizing intra-domain discrimination and minimizing inter-domain gaps is a crucial challenge for improving LReID performance. Most existing methods aim to reduce inter-domain gaps through knowledge distillation to maintain domain consistency. However, they often ignore intra-domain discrimination. To address this challenge, we propose a novel domain consistency representation learning (DCR) model that explores global and attribute-wise representations as a bridge to balance intra-domain discrimination and inter-domain gaps. At the intra-domain level, we explore the complementary relationship between global and attribute-wise representations to improve discrimination among similar identities. Excessive learning intra-domain discrimination can lead to catastrophic forgetting. We further develop an attribute-oriented anti-forgetting (AF) strategy that explores attribute-wise representations to enhance inter-domain consistency, and propose a knowledge consolidation (KC) strategy to facilitate knowledge transfer. Extensive experiments show that our DCR model achieves superior performance compared to state-of-the-art LReID methods. Our code will be available soon.

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