Abstract
Cross-modality person re-identification, particularly between daytime RGB images and nighttime infrared (IR) images, is one of the challenges in image retrieval tasks on the visual database. Cross-modality feature alignment is one typical solution in current works. However, the significant difference between RGB and IR modalities makes it difficult to align the two features directly. To reduce the modality gap, we introduce an intermediate modality, a grayscale image, which can be generated from RGB with the same label. The grayscale image retains the structural information of the RGB modality and exhibits a visual style similar to the IR image. To improve the performance of Re-ID in visible-infrared cross-modality tasks, we propose a Tri-modality Collaborative Learning model (TCL). There are two modules in TCL, the tri-modality joint feature extraction module and the modality-specific mean teaching classifier module. In the feature extraction module, multiple channels with different modalities are trained to learn modality-independent high-dimensional shared features. The classifier module is designed to ignore modality-specific information. We evaluate the effectiveness of TCL in the public dataset.
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Acknowledgments
Thanks to the corresponding author Dongyue Chen and other authors for their contributions. This work was supported by the National Natural Science Foundation of China (62202087, 62206043), Guangdong Basic and Applied Basic Research Foundation 2024A1515010244, the Fundamental Research Funds for the Central Universities (N2404008, N2404011, N2304020), and the 111 Project (B16009).
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Deng, S., Yang, Q., Yang, Z., Chen, D., Yang, Y., Wang, H. (2025). Tri-modality Collaborative Learning for Person Re-identification. In: Chen, T., Cao, Y., Nguyen, Q.V.H., Nguyen, T.T. (eds) Databases Theory and Applications. ADC 2024. Lecture Notes in Computer Science, vol 15449. Springer, Singapore. https://doi.org/10.1007/978-981-96-1242-0_24
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DOI: https://doi.org/10.1007/978-981-96-1242-0_24
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