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Titlebook: Computer Vision – ECCV 2020 Workshops; Glasgow, UK, August Adrien Bartoli,Andrea Fusiello Conference proceedings 2020 Springer Nature Swit

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AWNet: Attentive Wavelet Network for Image ISPelet transform, our proposed method enables us to restore favorable image details from RAW information and achieve a larger receptive field while remaining high efficiency in terms of computational cost. The global context block is adopted in our method to learn the non-local color mapping for the g
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FamilyGAN: Generating Kin Face Images Using Generative Adversarial Networksgle model. On the WVU Kinship Video database, the proposed model shows very promising results for generating kin images. Experimental results show 71.34% kinship verification accuracy using the images generated via FamilyGAN.
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Philip B. Whyman,Alina Ileana. Petrescuile at least maintaining PSNR of MSRResNet. The track had 150 registered participants, and 25 teams submitted the final results. They gauge the state-of-the-art in efficient single image super-resolution.
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Misconceptions about casinos and growth,es: defocus estimation, radiance, rendering, and upsampling. The four modules are trained on different sizes to learn global features as well as local details around the boundaries of in-focus objects. Experimental results show that our approach is capable of rendering a pleasing distinctive bokeh effect in complex scenes.
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https://doi.org/10.1007/978-1-349-01687-7experiments show the superiority of our model over the existing state-of-the-art. We participated in AIM 2020 efficient SR challenge with our MAFFSRN model and won 1st, 3rd, and 4th places in memory usage, floating-point operations (FLOPs) and number of parameters, respectively.
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The Economics of Casino Gamblingom the LRF hypothesis and ClipL1 loss, EEDNet can generate high-quality pictures with more details. Our method achieves promising results on Zurich RAW2RGB (ZRR) dataset and won the first place in AIM2020 ISP challenging.
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Misconceptions about casinos and growth,d their runtime on standard desktop CPUs as well as were running the models on smartphone GPUs. The proposed solutions significantly improved the baseline results, defining the state-of-the-art for practical bokeh effect rendering problem.
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Multi-attention Based Ultra Lightweight Image Super-Resolutionexperiments show the superiority of our model over the existing state-of-the-art. We participated in AIM 2020 efficient SR challenge with our MAFFSRN model and won 1st, 3rd, and 4th places in memory usage, floating-point operations (FLOPs) and number of parameters, respectively.
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