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Titlebook: Computer Vision – ACCV 2018 Workshops; 14th Asian Conferenc Gustavo Carneiro,Shaodi You Conference proceedings 2019 Springer Nature Switzer

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樓主: 夾子
21#
發(fā)表于 2025-3-25 03:20:40 | 只看該作者
https://doi.org/10.1007/978-1-4684-0409-8om 2 to 90?years old. Consequently, we demonstrated that the proposed method outperform existing methods based on both conventional machine learning frameworks for gait-based age estimation and a deep learning framework for gait recognition.
22#
發(fā)表于 2025-3-25 09:31:09 | 只看該作者
https://doi.org/10.1007/978-1-349-04387-3 designer-in-loop process of taking a generated image to production level design templates (tech-packs). Here the designers bring their own creativity by adding elements, suggestive from the generated image, to accentuate the overall aesthetics of the final design.
23#
發(fā)表于 2025-3-25 12:04:16 | 只看該作者
24#
發(fā)表于 2025-3-25 16:56:50 | 只看該作者
25#
發(fā)表于 2025-3-25 20:58:50 | 只看該作者
Let AI Clothe You: Diversified Fashion Generation designer-in-loop process of taking a generated image to production level design templates (tech-packs). Here the designers bring their own creativity by adding elements, suggestive from the generated image, to accentuate the overall aesthetics of the final design.
26#
發(fā)表于 2025-3-26 02:52:08 | 只看該作者
Word-Conditioned Image Style Transfere transfer in addition to a given word. We implemented the propose method by modifying the network for arbitrary neural artistic stylization. By the experiments, we show that the proposed method has ability to change the style of an input image taking account of both a given word.
27#
發(fā)表于 2025-3-26 07:21:19 | 只看該作者
28#
發(fā)表于 2025-3-26 12:18:49 | 只看該作者
29#
發(fā)表于 2025-3-26 16:01:27 | 只看該作者
30#
發(fā)表于 2025-3-26 19:34:50 | 只看該作者
Paying Attention to Style: Recognizing Photo Styles with Convolutional Attentional Unitsural activations. The proposed convolutional attentional units act as a filtering mechanism that conserves activations in convolutional blocks in order to contribute more meaningfully towards the visual style classes. State-of-the-art results were achieved on two large image style datasets, demonstrating the effectiveness of our method.
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