Diverse Representation Embedding for Lifelong Person Re-Identification

• URL: http://arxiv.org/abs/2403.16003v2
• Date: Tue, 2 Apr 2024 13:31:41 GMT
• Title: Diverse Representation Embedding for Lifelong Person Re-Identification
• Authors: Shiben Liu, Huijie Fan, Qiang Wang, Xiai Chen, Zhi Han, Yandong Tang,
• Abstract summary: Lifelong Person Re-Identification (LReID) aims to continuously learn from successive data streams, matching individuals across multiple cameras. Existing methods based on CNN backbone are insufficient to explore the representation of each instance from different perspectives. We propose a Diverse Representations Embedding (DRE) framework that first explores a pure transformer for LReID.
• Score: 10.824003066938234
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Lifelong Person Re-Identification (LReID) aims to continuously learn from successive data streams, matching individuals across multiple cameras. The key challenge for LReID is how to effectively preserve old knowledge while incrementally learning new information, which is caused by task-level domain gaps and limited old task datasets. Existing methods based on CNN backbone are insufficient to explore the representation of each instance from different perspectives, limiting model performance on limited old task datasets and new task datasets. Unlike these methods, we propose a Diverse Representations Embedding (DRE) framework that first explores a pure transformer for LReID. The proposed DRE preserves old knowledge while adapting to new information based on instance-level and task-level layout. Concretely, an Adaptive Constraint Module (ACM) is proposed to implement integration and push away operations between multiple overlapping representations generated by transformer-based backbone, obtaining rich and discriminative representations for each instance to improve adaptive ability of LReID. Based on the processed diverse representations, we propose Knowledge Update (KU) and Knowledge Preservation (KP) strategies at the task-level layout by introducing the adjustment model and the learner model. KU strategy enhances the adaptive learning ability of learner models for new information under the adjustment model prior, and KP strategy preserves old knowledge operated by representation-level alignment and logit-level supervision in limited old task datasets while guaranteeing the adaptive learning information capacity of the LReID model. Compared to state-of-the-art methods, our method achieves significantly improved performance in holistic, large-scale, and occluded datasets.

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