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

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书目名称Computer Vision – ECCV 2020 Workshops影响因子(影响力)




书目名称Computer Vision – ECCV 2020 Workshops影响因子(影响力)学科排名




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书目名称Computer Vision – ECCV 2020 Workshops网络公开度学科排名




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书目名称Computer Vision – ECCV 2020 Workshops被引频次学科排名




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书目名称Computer Vision – ECCV 2020 Workshops年度引用学科排名




书目名称Computer Vision – ECCV 2020 Workshops读者反馈




书目名称Computer Vision – ECCV 2020 Workshops读者反馈学科排名

妨碍

AIM 2020 Challenge on Video Temporal Super-Resolutions paper reports the second AIM challenge on Video Temporal Super-Resolution (VTSR), a.k.a. frame interpolation, with a focus on the proposed solutions, results, and analysis. From low-frame-rate (15 fps) videos, the challenge participants are required to submit higher-frame-rate (30 and 60 fps) sequ

诱惑

Enhanced Quadratic Video Interpolation upsurge in industry. Many learning-based methods have been proposed and achieved progressive results. Among them, a recent algorithm named quadratic video interpolation (QVI) achieves appealing performance. It exploits higher-order motion information (. acceleration) and successfully models the est

Aboveboard

Hippocampus

平庸的人或物

Multi-objective Reinforced Evolution in Mobile Neural Architecture Searchhitecture search involving evolutionary algorithms (EA) and reinforcement learning (RL), however, they are separately used. In this paper, we present a novel multi-objective algorithm called MoreMNAS (.ulti-.bjective .einforced .volution in .obile .eural .rchitecture .earch) by leveraging good virtu

平庸的人或物

哪有黄油

Deep Adaptive Inference Networks for Single Image Super-Resolution(CNNs). For most existing methods, the computational cost of each SISR model is irrelevant to local image content, hardware platform and application scenario. Nonetheless, content and resource adaptive model is more preferred, and it is encouraging to apply simpler and efficient networks to the easi

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湿润

Single Image Dehazing for a Variety of Haze Scenarios Using Back Projected Pyramid Networkrk architecture for this problem, namely back projected pyramid network (BPPNet), that gives good performance for a variety of challenging haze conditions, including dense haze and inhomogeneous haze. Our architecture incorporates learning of multiple levels of complexities while retaining spatial c