要求 发表于 2025-3-21 17:17:34
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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 发表于 2025-3-22 01:52:19
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 estAboveboard 发表于 2025-3-22 06:50:50
http://reply.papertrans.cn/24/2343/234238/234238_4.pngHippocampus 发表于 2025-3-22 09:52:27
http://reply.papertrans.cn/24/2343/234238/234238_5.png平庸的人或物 发表于 2025-3-22 14:31:52
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 发表于 2025-3-22 17:23:17
http://reply.papertrans.cn/24/2343/234238/234238_7.png哪有黄油 发表于 2025-3-22 23:45:26
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 easiFeedback 发表于 2025-3-23 05:10:31
http://reply.papertrans.cn/24/2343/234238/234238_9.png湿润 发表于 2025-3-23 07:26:55
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