VI-Diff: Unpaired Visible-Infrared Translation Diffusion Model for Single Modality Labeled Visible-Infrared Person Re-identification

• URL: http://arxiv.org/abs/2310.04122v1
• Date: Fri, 6 Oct 2023 09:42:12 GMT
• Title: VI-Diff: Unpaired Visible-Infrared Translation Diffusion Model for Single Modality Labeled Visible-Infrared Person Re-identification
• Authors: Han Huang, Yan Huang, Liang Wang
• Abstract summary: Cross-modality data annotation is costly and error-prone for Visible-Infrared person re-identification. We propose VI-Diff, a diffusion model that effectively addresses the task of Visible-Infrared person image translation. Our approach can be a promising solution to the VI-ReID task with single-modality labeled data and serves as a good starting point for future study.
• Score: 14.749167141971952
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Visible-Infrared person re-identification (VI-ReID) in real-world scenarios poses a significant challenge due to the high cost of cross-modality data annotation. Different sensing cameras, such as RGB/IR cameras for good/poor lighting conditions, make it costly and error-prone to identify the same person across modalities. To overcome this, we explore the use of single-modality labeled data for the VI-ReID task, which is more cost-effective and practical. By labeling pedestrians in only one modality (e.g., visible images) and retrieving in another modality (e.g., infrared images), we aim to create a training set containing both originally labeled and modality-translated data using unpaired image-to-image translation techniques. In this paper, we propose VI-Diff, a diffusion model that effectively addresses the task of Visible-Infrared person image translation. Through comprehensive experiments, we demonstrate that VI-Diff outperforms existing diffusion and GAN models, making it a promising solution for VI-ReID with single-modality labeled data. Our approach can be a promising solution to the VI-ReID task with single-modality labeled data and serves as a good starting point for future study. Code will be available.

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