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Titlebook: Individualizing Training Procedures with Wearable Technology; Peter Düking,Billy Sperlich Book 2024 The Editor(s) (if applicable) and The

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Carlos Balsalobre-Fernández,Manuel Matzkal visual features and high-level semantic features, allowing generating separate palette colors for different objects with similar colors. This enables users to perform targeted local editing, i.e., distinguish and recolor objects with similar colors separately, without producing unexpected global c
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Matthew Driller,Ian Dunican,Kari Lambing,Amy Benderlity prediction function from data. We evaluate the proposed method for different powerful FR models on two classical video-based (or template-based) benchmarks IJB-B and YTF. Extensive experiments show that, although the tinyFQnet is much smaller than the others, the proposed method outperforms sta
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Christoph Zinnerintra-video and inter-video loss. Moreover, a ranking weight strategy is presented to select high-quality positive and negative pairs during training. Afterward, an effective pseudo-label denoised process is introduced to alleviate the noisy activations caused by the video-level annotations, thereby
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Leon Forcher,Leander Forcher,Stefan Altmannategories to weight different classes, then adaptively leverage the suppression of head classes according to the logit value of the network output. Meanwhile, dynamically adjusting the suppression gradient of the background classes to protect the head and common classes while improving the detection
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Individualizing Training Procedures with Wearable Technology978-3-031-45113-3
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,Sensor Data from Wearable Technologies to Inform Decision-Making to Individualize Training Procedur in response to different training stimuli, transferability of training procedures between athletes or applying the same training stimuli at different time points to the same atletes is impaired. In order to make the right decision during training procedures for the individual, athletes and their co
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