Zobrazeno 1 - 10
of 31
pro vyhledávání: '"Junjie Ke"'
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Publikováno v:
Frontiers in Signal Processing, Vol 3 (2023)
No-reference video quality assessment (NR-VQA) for user generated content (UGC) is crucial for understanding and improving visual experience. Unlike video recognition tasks, VQA tasks are sensitive to changes in input resolution. Since large amounts
Externí odkaz:
https://doaj.org/article/c12d356f1ae24d3ca29d82bf1d365db8
Autor:
Junjie Ke, Xuchang Zhou, Yajing Yang, Hai Shen, Xiaobing Luo, Hui Liu, Lu Gao, Xin He, Xin Zhang
Publikováno v:
Frontiers in Physiology, Vol 13 (2022)
Purpose: To explore the effect of blood flow restriction training (BFRT) on the recovery of knee function in patients after arthroscopic partial meniscectomy (APM).Methods: Forty patients undergoing APM surgery were included in this parallel group, t
Externí odkaz:
https://doaj.org/article/e8faaf2df4f443b59c25f45215eb7c93
Synthesis of NiCo2S4@NiMoO4 Nanosheets with Excellent Electrochemical Performance for Supercapacitor
Publikováno v:
Crystals, Vol 12, Iss 6, p 821 (2022)
Currently, the research of energy storage devices mainly focuses on enhancing their electrochemical performance. Core-shell structured NiCo2S4@NiMoO4 is thought to be one of the most promising electrode materials for supercapacitors due to its high s
Externí odkaz:
https://doaj.org/article/f58f61e53a7b4d3482c135edeb36e2f9
Publikováno v:
2022 IEEE International Conference on Image Processing (ICIP).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::57f7ddcfbe5558f74146eb3844352f77
https://doi.org/10.1109/icip46576.2022.9897764
https://doi.org/10.1109/icip46576.2022.9897764
Autor:
Yilin Wang, Joong Gon Yim, Neil Birkbeck, Junjie Ke, Hossein Talebi, Xi Chen, Feng Yang, Balu Adsumilli
Publikováno v:
2022 IEEE International Conference on Image Processing (ICIP).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f4f376f6e8599930e7bd00505b463862
https://doi.org/10.1109/icip46576.2022.9897401
https://doi.org/10.1109/icip46576.2022.9897401
Autor:
Peyman Milanfar, Yilin Wang, Neil Birkbeck, Hossein Talebi, Balu Adsumilli, Joong Gon Yim, Junjie Ke, Feng Yang
Publikováno v:
CVPR
Video quality assessment for User Generated Content (UGC) is an important topic in both industry and academia. Most existing methods only focus on one aspect of the perceptual quality assessment, such as technical quality or compression artifacts. In
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9636af4ae9b812725b30b57ef6e1cba8
https://doi.org/10.1109/cvpr46437.2021.01323
https://doi.org/10.1109/cvpr46437.2021.01323
Publikováno v:
CVPR
Single domain generalization aims to learn a model that performs well on many unseen domains with only one domain data for training. Existing works focus on studying the adversarial domain augmentation (ADA) to improve the model's generalization capa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1d517159f54623e3b2d534bb5eb40d6a
http://arxiv.org/abs/2106.01899
http://arxiv.org/abs/2106.01899
Image quality assessment (IQA) is an important research topic for understanding and improving visual experience. The current state-of-the-art IQA methods are based on convolutional neural networks (CNNs). The performance of CNN-based models is often
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::442882281b480daf06011d73b705043a