Abstract
Person re-identification (ReID) has witnessed great progress in recent years. Existing approaches are able to achieve significant performance on the single dataset but fail to generalize well on another datasets. The emerging problem mainly comes from style difference between two datasets. To address this problem, we propose a novel style transfer framework based on Generative Adversarial Networks (GAN) to generate target-style images. Specifically, we get the style representation by calculating the Garm matrix of the three-channel original image, and then minimize the Euclidean distance of the style representation on different images transferred by the same generator while image generation. Finally, the labeled source dataset combined with the style-transferred images are all used to enhance the generalization ability of the ReID model. Experimental results suggest that the proposed strategy is very effective on the Market-1501 and DukeMTMC-reID.
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Acknowledgment
This work is supported in part by National Basic Research Program of China (973 Program): 2015CB351802, and Natural Science Foundation of China (NSFC): 61390501, 61876171 and 61572465.
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Xu, F., Ma, B., Chang, H., Shan, S., Chen, X. (2019). Style Transfer with Adversarial Learning for Cross-Dataset Person Re-identification. In: Jawahar, C., Li, H., Mori, G., Schindler, K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science(), vol 11366. Springer, Cham. https://doi.org/10.1007/978-3-030-20876-9_11
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