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• W4385196565 abstract "Recent years have witnessed the deployment of fully supervised gait recognition. However, due to domain diversity, gait recognition models designed under the fully supervised condition suffer from poor generalization in unseen domains. How to improve the generalization ability of gait recognition models and enhance their performance on unseen domains is still unexplored in existing gait recognition approaches. This paper investigate the generalizable gait recognition problem and proposes a Progressive Sub-domain Information Mining (PSIM) framework for single-source generalizable gait recognition. During training, PSIM can mine sub-domain information from a single large-scale source domain by differentiating gait features extracted from different people through unsupervised clustering. Then, domain information mitigation loss and domain homogenization loss are introduced to regularize those gait features to be domain insensitive. The above procedures is conducted iteratively until the model converges. Our PSIM framework is model-agnostic, which can directly improve the generalization ability of state-of-the-art gait recognition models without bringing too much complexity in model design. In experiments, our model-agnostic PSIM framework is adopted on several gait recognition models to show its effectiveness in boosting gait recognition performance for the single-source generalizable gait recognition task." @default.
• W4385196565 created "2023-07-25" @default.
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• W4385196565 date "2023-01-01" @default.
• W4385196565 modified "2023-10-17" @default.
• W4385196565 title "Progressive Sub-Domain Information Mining for Single-Source Generalizable Gait Recognition" @default.
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• W4385196565 doi "https://doi.org/10.1109/tifs.2023.3298518" @default.
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