Abstract
Cross-domain person re-identification (re-ID) is a challenging task in computer vision research. We intent to learn a discriminative model when given labeled source and unlabeled target datasets, in order to obtain good results on the target test datasets. In this paper, we add structure information for pedestrian images. To this end, we evaluate the influence of structure information for cross-domain re-ID in the deep networks and a series of experiments with different settings on Market-1501 and DukeMTMC-reID are conducted. The influence of structure information on the effectiveness in the optimized deep models is shown in the experimental results.
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Acknowledgements
This work was supported by National Training Program of Innovation and Entrepreneurship for Undergraduates under Grant No. 202010065021, Natural Science Foundation of Tianjin under Grant No. 20JCZDJC00180 and No. 19JCZDJC31500, the Open Projects Program of National Laboratory of Pattern Recognition under Grant No. 202000002, and the Tianjin Higher Education Creative Team Funds Program.
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Wang, N. et al. (2022). Influence of Structure Information for Cross-domain Person Re-identification. In: Liang, Q., Wang, W., Liu, X., Na, Z., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2021. Lecture Notes in Electrical Engineering, vol 878. Springer, Singapore. https://doi.org/10.1007/978-981-19-0390-8_7
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DOI: https://doi.org/10.1007/978-981-19-0390-8_7
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