Titlebook: Industrial Drying Systems; Guidelines for Agric J E ‘Ed‘ Smith Book 2023 The Editor(s) (if applicable) and The Author(s), under exclusive l

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J E ’Ed’ Smithle way of achieving few-shot learning is to realize a model that can rapidly adapt to the context of a given task. Dynamic networks have been shown capable of learning content-adaptive parameters efficiently, making them suitable for few-shot learning. In this paper, we propose to learn the dynamic

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J E ’Ed’ Smithted benchmark datasets. Recent progress in self-supervised human mesh recovery has been made using synthetic-data-driven training paradigms where the model is trained from synthetic paired 2D representation (.., 2D keypoints and segmentation masks) and 3D mesh. However, on synthetic dense correspond

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J E ’Ed’ Smithhe-art approaches fall quite short of capturing how human experts analyze sports scenes. There are several major reasons: (1) The used dataset is collected from non-official providers, which naturally creates a gap between models trained on those datasets and real-world applications; (2) previously

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