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Titlebook: Computer Vision - ACCV 2014 Workshops; Singapore, Singapore C. V. Jawahar,Shiguang Shan Conference proceedings 2015 Springer International

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發(fā)表于 2025-3-25 06:02:07 | 只看該作者
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發(fā)表于 2025-3-25 10:52:31 | 只看該作者
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發(fā)表于 2025-3-25 15:00:23 | 只看該作者
Computer Vision - ACCV 2014 Workshops978-3-319-16634-6Series ISSN 0302-9743 Series E-ISSN 1611-3349
24#
發(fā)表于 2025-3-25 16:14:26 | 只看該作者
0302-9743 h Vision Technology, the Third Workshop on E-Heritage, and the Workshop on Computer Vision for Affective Computing. LNCS 9010 contains the papers selected for the Workshop on Feature and Simila978-3-319-16633-9978-3-319-16634-6Series ISSN 0302-9743 Series E-ISSN 1611-3349
25#
發(fā)表于 2025-3-25 22:57:32 | 只看該作者
The Cultural Sociology of Art and Musicat help to discover visual attributes by clustering. However, the latent attribute structure in real life is multi-relational, e.g. two different sport cars in different poses vs. a sport car and a family car in the same pose - what attribute can dominate similarity? Instead of clustering, a network
26#
發(fā)表于 2025-3-26 00:57:27 | 只看該作者
27#
發(fā)表于 2025-3-26 05:56:39 | 只看該作者
Corporate Culture Out of Control?, LL-SVM is verified to find a balance between accuracy and efficiency. And among all the features including pixel value intensity, Gabor, LBP and HOG features, we find that HOG features are the most appropriate features to detect smiling faces, which, combined with RBF kernel SVM, achieve an accurac
28#
發(fā)表于 2025-3-26 11:10:31 | 只看該作者
https://doi.org/10.1007/978-1-137-11639-0 of values tested in cross validation process, . is the number of steps in the Conjugate Gradient method, . is the number of training data, . and . are the dimensions of projection matrix.). The experimental results of our method on UCI datasets for classification task and LFW dataset for face verif
29#
發(fā)表于 2025-3-26 16:15:45 | 只看該作者
30#
發(fā)表于 2025-3-26 18:07:05 | 只看該作者
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