2022 | OriginalPaper | Buchkapitel
OIMNet++
: Prototypical Normalization and Localization-Aware Learning for Person Search
verfasst von : Sanghoon Lee, Youngmin Oh, Donghyeon Baek, Junghyup Lee, Bumsub Ham
Erschienen in: Computer Vision – ECCV 2022
Verlag: Springer Nature Switzerland
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Abstract
++
that addresses the aforementioned limitations. To this end, we introduce a novel normalization layer, dubbed ProtoNorm, that calibrates features from pedestrian proposals, while considering a long-tail distribution of person IDs, enabling L2 normalized person representations to be discriminative. We also propose a localization-aware feature learning scheme that encourages better-aligned proposals to contribute more in learning discriminative representations. Experimental results and analysis on standard person search benchmarks demonstrate the effectiveness of OIMNet++
.