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

導師

https://doi.org/10.1007/978-3-319-26047-1ly 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

附錄

黑豹

多樣

https://doi.org/10.1007/978-3-662-65102-5their practical applicability. In this paper, we develop a computation efficient yet accurate network based on the proposed attentive auxiliary features (A.F) for SISR. Firstly, to explore the features from the bottom layers, the auxiliary feature from all the previous layers are projected into a co

莊嚴

Integrating the Engine in the Vehicle,he 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

使隔離

Computer Vision – ACCV 2020978-3-030-69532-3Series ISSN 0302-9743 Series E-ISSN 1611-3349

Detonate

Lecture Notes in Computer Sciencehttp://image.papertrans.cn/c/image/234127.jpg

起皺紋

Second-Order Camera-Aware Color Transformation for Cross-Domain Person Re-identification all the views of both source and target domain data with original ImageNet data statistics. This new input normalization method, as shown in our experiments, is much more efficient than simply using ImageNet statistics. We test our method on Market1501, DukeMTMC, and MSMT17 and achieve leading perf

laparoscopy

MCGKT-Net: Multi-level Context Gating Knowledge Transfer Network for Single Image Derainingg the knowledge already learned in other task domains. Furthermore, to dynamically select useful features in learning procedure, we propose a multi-scale context gating module in the MCGKT-Net using squeeze-and-excitation block. Experiments on three benchmark datasets: Rain100H, Rain100L, and Rain80

抵消

Degradation Model Learning for Real-World Single Image Super-Resolutionradation kernel as the weighted combination of the basis kernels. With the learned degradation model, a large number of realistic HR-LR pairs can be easily generated to train a more robust SISR model. Extensive experiments are performed to quantitatively and qualitatively validate the proposed degra