Self-Supervised Modality-Aware Multiple Granularity Pre-Training for RGB-Infrared Person Re-Identification

• URL: http://arxiv.org/abs/2112.06147v1
• Date: Sun, 12 Dec 2021 04:40:33 GMT
• Title: Self-Supervised Modality-Aware Multiple Granularity Pre-Training for RGB-Infrared Person Re-Identification
• Authors: Lin Wan, Qianyan Jing, Zongyuan Sun, Chuang Zhang, Zhihang Li, Yehansen Chen
• Abstract summary: Modality-Aware Multiple Granularity Learning (MMGL) is a self-supervised pre-training alternative to ImageNet pre-training. MMGL learns better representations (+6.47% Rank-1) with faster training speed (converge in few hours) and solider data efficiency (5% data size) than ImageNet pre-training. Results suggest it generalizes well to various existing models, losses and has promising transferability across datasets.
• Score: 9.624510941236837
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: While RGB-Infrared cross-modality person re-identification (RGB-IR ReID) has enabled great progress in 24-hour intelligent surveillance, state-of-the-arts still heavily rely on fine-tuning ImageNet pre-trained networks. Due to the single-modality nature, such large-scale pre-training may yield RGB-biased representations that hinder the performance of cross-modality image retrieval. This paper presents a self-supervised pre-training alternative, named Modality-Aware Multiple Granularity Learning (MMGL), which directly trains models from scratch on multi-modality ReID datasets, but achieving competitive results without external data and sophisticated tuning tricks. Specifically, MMGL globally maps shuffled RGB-IR images into a shared latent permutation space and further improves local discriminability by maximizing agreement between cycle-consistent RGB-IR image patches. Experiments demonstrate that MMGL learns better representations (+6.47% Rank-1) with faster training speed (converge in few hours) and solider data efficiency (<5% data size) than ImageNet pre-training. The results also suggest it generalizes well to various existing models, losses and has promising transferability across datasets. The code will be released.

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