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Titlebook: Computer Vision – ECCV 2018 Workshops; Munich, Germany, Sep Laura Leal-Taixé,Stefan Roth Conference proceedings 2019 Springer Nature Switze

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發(fā)表于 2025-3-27 00:42:10 | 只看該作者
32#
發(fā)表于 2025-3-27 02:30:46 | 只看該作者
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發(fā)表于 2025-3-27 06:51:40 | 只看該作者
34#
發(fā)表于 2025-3-27 10:43:18 | 只看該作者
The Unimaginable Place in Nature,m. Our experimental results on the Cityscapes dataset present state-of-the-art semantic segmentation predictions, and instance segmentation results outperforming a strong baseline based on optical flow.
35#
發(fā)表于 2025-3-27 16:30:24 | 只看該作者
Full-Body High-Resolution Anime Generation with Progressive Structure-Conditional Generative Adversation results of diverse anime characters at 1024?.?1024 based on target pose sequences. We also create a novel dataset containing full-body 1024?.?1024 high-resolution images and exact 2D pose keypoints using Unity 3D Avatar models.
36#
發(fā)表于 2025-3-27 18:43:57 | 只看該作者
37#
發(fā)表于 2025-3-27 22:29:26 | 只看該作者
Deep Learning for Automated Tagging of Fashion Imagess. Our prediction system hosts several classifiers working at scale to populate a catalogue of millions of products. We provide details of our models as well as the challenges involved in predicting Fashion attributes in a relatively homogeneous problem space.
38#
發(fā)表于 2025-3-28 02:07:00 | 只看該作者
Brand > Logo: Visual Analysis of Fashion Brandsuch as color, patterns and shapes. In this work, we analyze learned visual representations by deep networks that are trained to recognize fashion brands. In particular, the activation strength and extent of neurons are studied to provide interesting insights about visual brand expressions. The propo
39#
發(fā)表于 2025-3-28 10:09:07 | 只看該作者
40#
發(fā)表于 2025-3-28 14:15:17 | 只看該作者
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