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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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書目名稱Computer Vision – ECCV 2020 Workshops
副標(biāo)題Glasgow, UK, August
編輯Adrien Bartoli,Andrea Fusiello
視頻videohttp://file.papertrans.cn/235/234236/234236.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Computer Vision – ECCV 2020 Workshops; Glasgow, UK, August  Adrien Bartoli,Andrea Fusiello Conference proceedings 2020 Springer Nature Swit
描述.The 6-volume set, comprising the LNCS books 12535 until 12540, constitutes the refereed proceedings of 28 out of the 45 workshops held at the 16th European Conference on Computer Vision, ECCV 2020. The conference was planned to take place in Glasgow, UK, during August 23-28, 2020, but changed to a virtual format due to the COVID-19 pandemic..The 249 full papers, 18 short papers, and 21 further contributions included in the workshop proceedings were carefully reviewed and selected from a total of 467 submissions. The papers deal with diverse computer vision topics..Part III includes the Advances in Image Manipulation Workshop and Challenges.?.
出版日期Conference proceedings 2020
關(guān)鍵詞color image processing; computer vision; digital image; face recognition; image analysis; image coding; im
版次1
doihttps://doi.org/10.1007/978-3-030-67070-2
isbn_softcover978-3-030-67069-6
isbn_ebook978-3-030-67070-2Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2020
The information of publication is updating

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Philip B. Whyman,Alina I. Petrescucombines the nearest-neighbor upsampling, convolution and PA layers. It improves the final reconstruction quality with little parameter cost. Our final model—PAN could achieve similar performance as the lightweight networks—SRResNet and CARN, but with only 272K parameters (17.92% of SRResNet and 17.
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The Economics of Business Investment Abroadndependently. Finally, we present an adaptive hybrid composition based super-resolution network (AHCSRN) by pruning the baseline model. Extensive experiments demonstrate that the proposed method can achieve better performance than state-of-the-art SR models with ultra-low parameters and Flops.
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https://doi.org/10.1007/978-3-540-35104-7d extra data during training phase to compensate dropped performance. The experiments show that IdleSR can achieve a much better tradeoff among parameter, runtime and performance than start-of-the-art methods.
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Misconceptions about casinos and growth,elet 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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The Economics of Casino Gamblinggle 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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