A Broader Study of Spectral Missing in Multi-spectral Vehicle Re-identification
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-0827-7_5
Reference28 articles.
1. Kamenou, E., Del Rincón, J.M., Miller, P., Devlin-Hill, P.: A meta-learning approach for domain generalisation across visual modalities in vehicle re-identification, pp. 385–393 (2023)
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3. He, Q., Lu, Z., Wang, Z., Hu, H.: Graph-based progressive fusion network for multi-modality vehicle re-identification. In: IEEE Transactions on Intelligent Transportation Systems, pp. 1–17 (2023)
4. H.B. A., P.M.M. M., V. U., M.P. R., Enhanced vehicle re-identification for ITS: a feature fusion approach using deep learning. In: Proceedings of 2022 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), pp. 1–6 (2022)
5. Zheng, A., Zhu, X., Ma, Z., Li, C., Tang, J., Ma, J.: Cross-directional consistency network with adaptive layer normalization for multi-spectral vehicle re-identification and a high-quality benchmark. Inf. Fusion 101901 (2023)
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