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Titlebook: Computer Vision - ECCV 2014 Workshops; Zurich, Switzerland, Lourdes Agapito,Michael M. Bronstein,Carsten Rothe Conference proceedings 2015

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發(fā)表于 2025-3-25 04:56:25 | 只看該作者
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https://doi.org/10.1057/9781137464354ystem without having to implement a Pedestrian Detector algorithm yourself. We also provide body-part detection data on top of the manually labeled data and the Pedestrian Detection data, such as to make it trivial to extract your features from relevant local regions (actual body-parts). Finally we
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發(fā)表于 2025-3-25 13:37:59 | 只看該作者
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Multi-Modal Distance Metric Learning: ABayesian Non-parametric Approachthe flexible Beta process model, we can infer the dimensionality of the hidden space using training data itself. We also develop a novel Variational Bayes (VB) algorithm to compute the posterior distribution of the parameters that imposes the constraints (similarity/dissimilarity constraints) direct
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Learning Skeleton Stream Patterns with Slow Feature Analysis for Action Recognitionms. Then, Slow Feature Analysis is applied to learn the visual pattern of each joint, and the high-level information in the learnt general patterns is encoded into each skeleton to reduce the intra-variance of the skeletons. Temporal pyramid of posture word histograms is used to describe the global
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發(fā)表于 2025-3-26 04:19:40 | 只看該作者
The HDA+ Data Set for Research on Fully Automated Re-identification Systemsystem without having to implement a Pedestrian Detector algorithm yourself. We also provide body-part detection data on top of the manually labeled data and the Pedestrian Detection data, such as to make it trivial to extract your features from relevant local regions (actual body-parts). Finally we
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發(fā)表于 2025-3-26 10:34:27 | 只看該作者
Computer Vision - ECCV 2014 Workshops978-3-319-16199-0Series ISSN 0302-9743 Series E-ISSN 1611-3349
29#
發(fā)表于 2025-3-26 13:34:30 | 只看該作者
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發(fā)表于 2025-3-26 16:51:52 | 只看該作者
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