Abstract
Cross-Modality Re-Identification (CM-ReID) between infrared images (IR) and color image is an extended task of person ReID, which is mainly responsible for retrieving the specified object at night or in dim environment. It remains challenging due to inter-camera and cross-modality. Targeting to solve these problems, this paper proposed a cross-modality ReID via shared features and re-ranking. It consists of a multi-branch network with attention mechanism and rethinking re-ranking strategy. The network explicitly leverages the global context block and cross-modality constraint to learn distinguished representations, which builds a retrieved bridge between IR and RGB by mining cross-modality shared space. The global context block can capture more context information from original image, and the cross-modality constraint (CrMC) can reduce the possibility of high-dimensional shared feature space. Besides, the improved re-ranking strategy is introduced to further optimize the initial results. Although the proposed method is simple, extensive experimental results demonstrate that it significantly outperforms the state-of-the-art approaches on CM-ReID datasets.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China under Grant No.62002247, the Open Project Program of State Key Laboratory of Virtual Reality Technology and Systems, Beihang University (No. VRLAB2020A03), and the general project numbered KM202110028009 of Beijing Municipal Education Commission.
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Jiang, N., Wang, Z., Xu, P., Wu, X., Zhang, L. (2022). Rethinking Shared Features and Re-ranking for Cross-Modality Person Re-identification. In: Þór Jónsson, B., et al. MultiMedia Modeling. MMM 2022. Lecture Notes in Computer Science, vol 13142. Springer, Cham. https://doi.org/10.1007/978-3-030-98355-0_26
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