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. This project aims to efficiently manage unannotated or partially labeled datasets by utilizing weakly-supervised and self-supervised learning, enabling scalable and cost-effective solutions. The outcome will
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improved health and sustainability, urban planning policy and guidance need to set clear goals and visions for walking for all. This project is a part of the Research Program for Walking (Financed by
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