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.

提出了 VI-Diff，一种有效解决可见 - 红外人物图像翻译任务的扩散模型。通过综合实验证明，VI-Diff 优于现有的扩散和生成对抗网络模型，为具有单模态标记数据的 VI-ReID 任务提供了有希望的解决方案。