Continuous Learning without Forgetting for Person Re-Identification

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Sugianto, Nehemia
Tjondronegoro, Dian
Sorwar, Golam
Chakraborty, Prithwi
Yuwono, Elizabeth Irenne
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2019
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Taipei, Taiwan

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Abstract

Deep learning-based person re-identification faces a scalability challenge when the target domain requires continuous learning. Service environments, such as airports, need to recognize new visitors and add new cameras over time. Training-at-once is not enough to make the model robust to new tasks and domain variations. A well-known approach is fine-tuning, which suffers forgetting problem on old tasks when learning new tasks. Joint-training can alleviate the problem but requires old datasets, which is unobtainable in some cases. Recently, Learning without forgetting (LwF) shows its ability to mitigate the problem without old datasets. This paper extends the benefit of LwF from image classification to person re-identification with further challenges. Comprehensive experiments are based on Market1501 and DukeMTMC4ReID to evaluate and benchmark LwF to other approaches. The results confirm that LwF outperforms fine-tuning in preserving old knowledge and joint-training in faster training.

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2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

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© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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Artificial intelligence

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Sugianto, N; Tjondronegoro, D; Sorwar, G; Chakraborty, P; Yuwono, EI, Continuous Learning without Forgetting for Person Re-Identification, 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2019