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Titlebook: Image and Graphics; 12th International C Huchuan Lu,Wanli Ouyang,Min Xu Conference proceedings 2023 The Editor(s) (if applicable) and The A

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51#
發(fā)表于 2025-3-30 11:29:38 | 只看該作者
Image Super-Resolution via Deep Dictionary Learningthe deep features of the image, but also the trained dictionary may be relatively large. This paper proposes a new deep dictionary learning model. First, after preprocessing the images of the training set, the dictionary is trained by the deep dictionary learning method, and the super-resolution rec
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發(fā)表于 2025-3-30 14:50:45 | 只看該作者
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發(fā)表于 2025-3-30 17:05:52 | 只看該作者
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發(fā)表于 2025-3-31 02:51:00 | 只看該作者
56#
發(fā)表于 2025-3-31 05:27:41 | 只看該作者
Research on?Strategies for?Tripeaks Variant with?Various Layoutseaks variant games and assist in level design, this paper investigates the playing strategies for Tripeaks variant games. Firstly, three heuristic strategies based on player experience are proposed. Then, reinforcement learning agents are trained and tested on different datasets to evaluate their ge
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發(fā)表于 2025-3-31 09:29:37 | 只看該作者
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發(fā)表于 2025-3-31 13:26:27 | 只看該作者
ACMA-GAN: Adaptive Cross-Modal Attention for?Text-to-Image Generationd. Recently, multi-stage conditional generative adversarial networks based on word attention are the mainstream of Text-to-Image generation. A close examination of these methods reveals two fundamental issues. Firstly, the granularity difference between the words and local image features makes the w
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發(fā)表于 2025-3-31 19:04:06 | 只看該作者
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發(fā)表于 2025-4-1 00:39:39 | 只看該作者
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