Abstract
To cope with the problem caused by inadequate training data, many person re-identification (re-id) methods exploited generative adversarial networks (GAN) for data augmentation, where the training of GAN is typically independent of that of the re-id model. The coupling relation between them which probably brings in a performance gain of re-id is thus ignored. In this work, we propose a general framework to jointly train GAN and the re-id model. It can simultaneously achieve the optima of both the generator and the re-id model, where the training is guided by each other through a discriminator. The re-id model is boosted for two reasons: 1) The adversarial training that encourages it to fool the discriminator; 2) The generated samples that augment the training data. Extensive results on benchmark datasets show that for the re-id model trained with the identification loss as well as the triplet loss, the proposed joint training framework outperforms existing methods with separated training and achieves state-of-the-art re-id performance.
Supported by the National Natural Science Foundation of China under Grant U1913204, Shandong Major Scientific and Technological Innovation Project 2018CXGC1503, and Qilu Young Scholars Program of Shandong University No. 31400082063101.
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Zhao, Z., Song, R., Zhang, Q., Duan, P., Zhang, Y. (2021). A Framework for Jointly Training GAN with Person Re-Identification Model. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12664. Springer, Cham. https://doi.org/10.1007/978-3-030-68799-1_3
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