Titlebook: Computer Vision – ACCV 2020; 15th Asian Conferenc Hiroshi Ishikawa,Cheng-Lin Liu,Jianbo Shi Conference proceedings 2021 Springer Nature Swi

Conduit

Leaven

闷热

一条卷发

An Efficient Group Feature Fusion Residual Network for Image Super-Resolutionly to improve their performance by simply increasing the depth of their network. Although this strategy can get promising results, it is inefficient in many real-world scenarios because of the high computational cost. In this paper, we propose an efficient group feature fusion residual network (GFFR

逢迎春日

conduct

Spartan

peptic-ulcer

Multi-scale Attentive Residual Dense Network for Single Image Rain Removalhe investigation on rain removal has thus been attracting, while the performances of existing deraining have limitations owing to over smoothing effect, poor generalization capability and rain intensity varies both in spatial locations and color channels. To address these issues, we proposed a Multi

事情

Conference proceedings 2021n November/ December 2020.*.The total of 254 contributions was carefully reviewed and selected from 768 submissions during two rounds of reviewing and improvement. The papers focus on the following topics:..Part I: 3D computer vision; segmentation and grouping..Part II: low-level vision, image proce

EVEN

Conference proceedings 2021for computer vision, generative models for computer vision..Part V: face, pose, action, and gesture; video analysis and event recognition; biomedical image analysis..Part VI: applications of computer vision; vision for X; datasets and performance analysis..*The conference was held virtually..