Zobrazeno 1 - 10
of 155
pro vyhledávání: '"Lu, Yiting"'
User Generated Content (UGC) videos are susceptible to complicated and variant degradations and contents, which prevents the existing blind video quality assessment (BVQA) models from good performance since the lack of the adapability of distortions
Externí odkaz:
http://arxiv.org/abs/2406.16297
In this work, we take the first exploration of the recently popular foundation model, i.e., State Space Model/Mamba, in image quality assessment, aiming at observing and excavating the perception potential in vision Mamba. A series of works on Mamba
Externí odkaz:
http://arxiv.org/abs/2406.09546
Autor:
Li, Bingchen, Li, Xin, Lu, Yiting, Feng, Ruoyu, Guo, Mengxi, Zhao, Shijie, Zhang, Li, Chen, Zhibo
Blind Compressed Image Restoration (CIR) has garnered significant attention due to its practical applications. It aims to mitigate compression artifacts caused by unknown quality factors, particularly with JPEG codecs. Existing works on blind CIR oft
Externí odkaz:
http://arxiv.org/abs/2404.17433
Autor:
Liu, Xiaohong, Min, Xiongkuo, Zhai, Guangtao, Li, Chunyi, Kou, Tengchuan, Sun, Wei, Wu, Haoning, Gao, Yixuan, Cao, Yuqin, Zhang, Zicheng, Wu, Xiele, Timofte, Radu, Peng, Fei, Fu, Huiyuan, Ming, Anlong, Wang, Chuanming, Ma, Huadong, He, Shuai, Dou, Zifei, Chen, Shu, Zhang, Huacong, Xie, Haiyi, Wang, Chengwei, Chen, Baoying, Zeng, Jishen, Yang, Jianquan, Wang, Weigang, Fang, Xi, Lv, Xiaoxin, Yan, Jun, Zhi, Tianwu, Zhang, Yabin, Li, Yaohui, Li, Yang, Xu, Jingwen, Liu, Jianzhao, Liao, Yiting, Li, Junlin, Yu, Zihao, Lu, Yiting, Li, Xin, Motamednia, Hossein, Hosseini-Benvidi, S. Farhad, Guan, Fengbin, Mahmoudi-Aznaveh, Ahmad, Mansouri, Azadeh, Gankhuyag, Ganzorig, Yoon, Kihwan, Xu, Yifang, Fan, Haotian, Kong, Fangyuan, Zhao, Shiling, Dong, Weifeng, Yin, Haibing, Zhu, Li, Wang, Zhiling, Huang, Bingchen, Saha, Avinab, Mishra, Sandeep, Gupta, Shashank, Sureddi, Rajesh, Saha, Oindrila, Celona, Luigi, Bianco, Simone, Napoletano, Paolo, Schettini, Raimondo, Yang, Junfeng, Fu, Jing, Zhang, Wei, Cao, Wenzhi, Liu, Limei, Peng, Han, Yuan, Weijun, Li, Zhan, Cheng, Yihang, Deng, Yifan, Li, Haohui, Qu, Bowen, Li, Yao, Luo, Shuqing, Wang, Shunzhou, Gao, Wei, Lu, Zihao, Conde, Marcos V., Wang, Xinrui, Chen, Zhibo, Liao, Ruling, Ye, Yan, Wang, Qiulin, Li, Bing, Zhou, Zhaokun, Geng, Miao, Chen, Rui, Tao, Xin, Liang, Xiaoyu, Sun, Shangkun, Ma, Xingyuan, Li, Jiaze, Yang, Mengduo, Xu, Haoran, Zhou, Jie, Zhu, Shiding, Yu, Bohan, Chen, Pengfei, Xu, Xinrui, Shen, Jiabin, Duan, Zhichao, Asadi, Erfan, Liu, Jiahe, Yan, Qi, Qu, Youran, Zeng, Xiaohui, Wang, Lele, Liao, Renjie
This paper reports on the NTIRE 2024 Quality Assessment of AI-Generated Content Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2024. This challenge is to address a major
Externí odkaz:
http://arxiv.org/abs/2404.16687
Autor:
Li, Xin, Yuan, Kun, Pei, Yajing, Lu, Yiting, Sun, Ming, Zhou, Chao, Chen, Zhibo, Timofte, Radu, Sun, Wei, Wu, Haoning, Zhang, Zicheng, Jia, Jun, Zhang, Zhichao, Cao, Linhan, Chen, Qiubo, Min, Xiongkuo, Lin, Weisi, Zhai, Guangtao, Sun, Jianhui, Wang, Tianyi, Li, Lei, Kong, Han, Wang, Wenxuan, Li, Bing, Luo, Cheng, Wang, Haiqiang, Chen, Xiangguang, Meng, Wenhui, Pan, Xiang, Shi, Huiying, Zhu, Han, Xu, Xiaozhong, Sun, Lei, Chen, Zhenzhong, Liu, Shan, Kong, Fangyuan, Fan, Haotian, Xu, Yifang, Xu, Haoran, Yang, Mengduo, Zhou, Jie, Li, Jiaze, Wen, Shijie, Xu, Mai, Li, Da, Yao, Shunyu, Du, Jiazhi, Zuo, Wangmeng, Li, Zhibo, He, Shuai, Ming, Anlong, Fu, Huiyuan, Ma, Huadong, Wu, Yong, Xue, Fie, Zhao, Guozhi, Du, Lina, Guo, Jie, Zhang, Yu, Zheng, Huimin, Chen, Junhao, Liu, Yue, Zhou, Dulan, Xu, Kele, Xu, Qisheng, Sun, Tao, Ding, Zhixiang, Hu, Yuhang
This paper reviews the NTIRE 2024 Challenge on Shortform UGC Video Quality Assessment (S-UGC VQA), where various excellent solutions are submitted and evaluated on the collected dataset KVQ from popular short-form video platform, i.e., Kuaishou/Kwai
Externí odkaz:
http://arxiv.org/abs/2404.11313
At present, large multimodal models (LMMs) have exhibited impressive generalization capabilities in understanding and generating visual signals. However, they currently still lack sufficient capability to perceive low-level visual quality akin to hum
Externí odkaz:
http://arxiv.org/abs/2403.12806
Autor:
Lu, Yiting, Li, Xin, Pei, Yajing, Yuan, Kun, Xie, Qizhi, Qu, Yunpeng, Sun, Ming, Zhou, Chao, Chen, Zhibo
Short-form UGC video platforms, like Kwai and TikTok, have been an emerging and irreplaceable mainstream media form, thriving on user-friendly engagement, and kaleidoscope creation, etc. However, the advancing content-generation modes, e.g., special
Externí odkaz:
http://arxiv.org/abs/2402.07220
In FMCW automotive radar applications, it is often a challenge to design a chirp sequence that satisfies the requirements set by practical driving scenarios and simultaneously enables high range resolution, large maximum range, and unambiguous veloci
Externí odkaz:
http://arxiv.org/abs/2312.01123
Blind Image Quality Assessment (BIQA) is susceptible to poor transferability when the distribution shift occurs, e.g., from synthesis degradation to authentic degradation. To mitigate this, some studies have attempted to design unsupervised domain ad
Externí odkaz:
http://arxiv.org/abs/2309.17014
Publikováno v:
Zhongguo quanke yixue, Vol 27, Iss 11, Pp 1288-1295 (2024)
Multimorbidity increases the burden of disease and treatment for patients, which is becoming an essential research issue in the field of public health and primary care. As medical research advances, the understanding of how to deal with the challenge
Externí odkaz:
https://doaj.org/article/d614169d2a674d50980972603f9e5f76