Zobrazeno 1 - 10
of 11
pro vyhledávání: '"Wu, Haoning"'
Autor:
Liu, Xiaohong, Min, Xiongkuo, Sun, Wei, Zhang, Yulun, Zhang, Kai, Timofte, Radu, Zhai, Guangtao, Gao, Yixuan, Cao, Yuqin, Kou, Tengchuan, Dong, Yunlong, Jia, Ziheng, Li, Yilin, Wu, Wei, Hu, Shuming, Deng, Sibin, Xiao, Pengxiang, Chen, Ying, Li, Kai, Zhao, Kai, Yuan, Kun, Sun, Ming, Cong, Heng, Wang, Hao, Fu, Lingzhi, Zhang, Yusheng, Zhang, Rongyu, Shi, Hang, Xu, Qihang, Xiao, Longan, Ma, Zhiliang, Agarla, Mirko, Celona, Luigi, Rota, Claudio, Schettini, Raimondo, Huang, Zhiwei, Li, Yanan, Wang, Xiaotao, Lei, Lei, Liu, Hongye, Hong, Wei, Chuang, Ironhead, Lin, Allen, Guan, Drake, Chen, Iris, Lou, Kae, Huang, Willy, Tasi, Yachun, Kao, Yvonne, Fan, Haotian, Kong, Fangyuan, Zhou, Shiqi, Liu, Hao, Lai, Yu, Chen, Shanshan, Wang, Wenqi, Wu, Haoning, Chen, Chaofeng, Zhu, Chunzheng, Guo, Zekun, Zhao, Shiling, Yin, Haibing, Wang, Hongkui, Meftah, Hanene Brachemi, Fezza, Sid Ahmed, Hamidouche, Wassim, Déforges, Olivier, Shi, Tengfei, Mansouri, Azadeh, Motamednia, Hossein, Bakhtiari, Amir Hossein, Aznaveh, Ahmad Mahmoudi
This paper reports on the NTIRE 2023 Quality Assessment of Video Enhancement Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2023. This challenge is to address a major cha
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3ee7b491469e09101b8811238090fb4d
http://arxiv.org/abs/2307.09729
http://arxiv.org/abs/2307.09729
Generative models have recently exhibited exceptional capabilities in various scenarios, for example, image generation based on text description. In this work, we focus on the task of generating a series of coherent image sequence based on a given st
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b2ef3cb74af5854a2874b621c1864e00
http://arxiv.org/abs/2306.00973
http://arxiv.org/abs/2306.00973
Autor:
Wu, Haoning, Liao, Liang, Wang, Annan, Chen, Chaofeng, Hou, Jingwen, Sun, Wenxiu, Yan, Qiong, Lin, Weisi
The proliferation of videos collected during in-the-wild natural settings has pushed the development of effective Video Quality Assessment (VQA) methodologies. Contemporary supervised opinion-driven VQA strategies predominantly hinge on training from
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ee08bcb728fdc98933949cacb6680431
http://arxiv.org/abs/2304.14672
http://arxiv.org/abs/2304.14672
Video frame interpolation (VFI) is a challenging task that aims to generate intermediate frames between two consecutive frames in a video. Existing learning-based VFI methods have achieved great success, but they still suffer from limited generalizat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e16af777d8206601208644014bbc0602
Autor:
Wu, Haoning, Zhang, Erli, Liao, Liang, Chen, Chaofeng, Hou, Jingwen, Wang, Annan, Sun, Wenxiu, Yan, Qiong, Lin, Weisi
The proliferation of in-the-wild videos has greatly expanded the Video Quality Assessment (VQA) problem. Unlike early definitions that usually focus on limited distortion types, VQA on in-the-wild videos is especially challenging as it could be affec
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bd73ff8539fc53b33f8181a90bc45ffb
Autor:
Wu, Haoning, Liao, Liang, Hou, Jingwen, Chen, Chaofeng, Zhang, Erli, Wang, Annan, Sun, Wenxiu, Yan, Qiong, Lin, Weisi
Recent learning-based video quality assessment (VQA) algorithms are expensive to implement due to the cost of data collection of human quality opinions, and are less robust across various scenarios due to the biases of these opinions. This motivates
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::97b76149d4451d1a05cbbb3b77f5fe21
Autor:
Li, Chunyi, Zhang, Zicheng, Wu, Haoning, Sun, Wei, Min, Xiongkuo, Liu, Xiaohong, Zhai, Guangtao, Lin, Weisi
With the rapid advancements of the text-to-image generative model, AI-generated images (AGIs) have been widely applied to entertainment, education, social media, etc. However, considering the large quality variance among different AGIs, there is an u
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f4e27e371a8420821bf6a2127a6c886f
Autor:
Wu, Haoning, Chen, Chaofeng, Liao, Liang, Hou, Jingwen, Sun, Wenxiu, Yan, Qiong, Gu, Jinwei, Lin, Weisi
The increased resolution of real-world videos presents a dilemma between efficiency and accuracy for deep Video Quality Assessment (VQA). On the one hand, keeping the original resolution will lead to unacceptable computational costs. On the other han
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3d47ab8ec745661dd1cc37caedfb64f1
Autor:
Wu, Haoning, Zhang, Erli, Liao, Liang, Chen, Chaofeng, Hou, Jingwen, Wang, Annan, Sun, Wenxiu, Yan, Qiong, Lin, Weisi
The rapid increase in user-generated-content (UGC) videos calls for the development of effective video quality assessment (VQA) algorithms. However, the objective of the UGC-VQA problem is still ambiguous and can be viewed from two perspectives: the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e01db5fdab09eaf33962c5c0a8b05e59
Autor:
Wu, Haoning, Chen, Chaofeng, Hou, Jingwen, Liao, Liang, Wang, Annan, Sun, Wenxiu, Yan, Qiong, Lin, Weisi
Current deep video quality assessment (VQA) methods are usually with high computational costs when evaluating high-resolution videos. This cost hinders them from learning better video-quality-related representations via end-to-end training. Existing
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::77db102c3ec42bdef20498e1fa243a89