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Titlebook: Computer Vision – ECCV 2022; 17th European Confer Shai Avidan,Gabriel Brostow,Tal Hassner Conference proceedings 2022 The Editor(s) (if app

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21#
發(fā)表于 2025-3-25 05:33:29 | 只看該作者
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發(fā)表于 2025-3-25 11:09:09 | 只看該作者
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發(fā)表于 2025-3-25 15:17:44 | 只看該作者
24#
發(fā)表于 2025-3-25 19:07:13 | 只看該作者
,Multi-Curve Translator for?High-Resolution Photorealistic Image Translation,onal cost. Besides, MCT is a plug-in approach that utilizes existing base models and requires only replacing their output layers. Experiments demonstrate that the MCT variants can process 4K images in real-time and achieve comparable or even better performance than the base models on various photorealistic image-to-image translation tasks.
25#
發(fā)表于 2025-3-25 21:27:56 | 只看該作者
,Cross Attention Based Style Distribution for?Controllable Person Image Synthesis,ode the source appearance accurately, the self attention among different semantic styles is also added. The effectiveness of our model is validated quantitatively and qualitatively on pose transfer and virtual try-on tasks. Codes are available at ..
26#
發(fā)表于 2025-3-26 04:08:25 | 只看該作者
27#
發(fā)表于 2025-3-26 04:33:46 | 只看該作者
28#
發(fā)表于 2025-3-26 09:38:13 | 只看該作者
Deep Bayesian Video Frame Interpolation, the input observations. With this approach we show new records on 8 of 10 benchmarks, using an architecture with half the parameters of the state-of-the-art model. Code and models are publicly available at ..
29#
發(fā)表于 2025-3-26 15:08:50 | 只看該作者
https://doi.org/10.1007/978-1-349-08919-2s. However, previous approaches struggle to synthesize high-frequency signals with fine details, deteriorating the synthesis quality. To address this, we propose WaveGAN, a frequency-aware model for few-shot image generation. Concretely, we disentangle encoded features into multiple frequency compon
30#
發(fā)表于 2025-3-26 17:14:35 | 只看該作者
Can Arms Races Lead to the Outbreak of War? from prior works, we solve this problem by learning a conditional probability distribution of the edits, . in code space. Training such a model requires addressing the lack of example edits for training. To this end, we propose a self-supervised approach that simulates edits by augmenting off-the-s
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