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• W4313145960 abstract "Incremental learning (IL) remains an open issue for Person Re-identification (ReID), where a ReID system is expected to preserve preceding knowledge while learning incrementally. However, due to the strict privacy licenses and the open-set retrieval setting, it is intractable to adapt existing class IL methods to ReID. In this work, we propose an Augmented Geometric Distillation (AGD) framework to tackle these issues. First, a general data-free incremental framework with dreaming memory is constructed to avoid privacy disclosure. On this basis, we reveal a “noisy distillation” problem stemming from the noise in dreaming memory, and further propose to augment distillation in a pairwise and cross-wise pattern over different views of memory to mitigate it. Second, for the open-set retrieval property, we propose to maintain feature space structure during evolving via a novel geometric way and preserve relationships between exemplars when representations drift. Extensive experiments demonstrate the superiority of our AGD to baseline with a margin of 6.0% mAP/7.9% R@1 and it could be generalized to class IL. Code is available here <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>1</sup> <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>1</sup> †https://github.com/eddielyc/Augmented-Geometric-Distillation." @default.
• W4313145960 created "2023-01-06" @default.
• W4313145960 creator A5021997733 @default.
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• W4313145960 date "2022-06-01" @default.
• W4313145960 modified "2023-09-27" @default.
• W4313145960 title "Augmented Geometric Distillation for Data-Free Incremental Person ReID" @default.
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• W4313145960 doi "https://doi.org/10.1109/cvpr52688.2022.00718" @default.
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