Zobrazeno 1 - 10
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pro vyhledávání: '"Li, Chunyi"'
Autor:
Li, Chunyi, Wu, Xiele, Wu, Haoning, Feng, Donghui, Zhang, Zicheng, Lu, Guo, Min, Xiongkuo, Liu, Xiaohong, Zhai, Guangtao, Lin, Weisi
Ultra-low bitrate image compression is a challenging and demanding topic. With the development of Large Multimodal Models (LMMs), a Cross Modality Compression (CMC) paradigm of Image-Text-Image has emerged. Compared with traditional codecs, this sema
Externí odkaz:
http://arxiv.org/abs/2406.09356
We study moduli spaces of stable objects in the Kuznetsov components of Fano threefolds. We prove a general non-emptiness criterion for moduli spaces, which applies to the cases of prime Fano threefolds of index $1$, degree $10 \leq d \leq 18$, and i
Externí odkaz:
http://arxiv.org/abs/2406.09124
Autor:
Zhang, Zicheng, Wu, Haoning, Li, Chunyi, Zhou, Yingjie, Sun, Wei, Min, Xiongkuo, Chen, Zijian, Liu, Xiaohong, Lin, Weisi, Zhai, Guangtao
How to accurately and efficiently assess AI-generated images (AIGIs) remains a critical challenge for generative models. Given the high costs and extensive time commitments required for user studies, many researchers have turned towards employing lar
Externí odkaz:
http://arxiv.org/abs/2406.03070
Autor:
Zhou, Xunchu, Liu, Xiaohong, Dong, Yunlong, Kou, Tengchuan, Gao, Yixuan, Zhang, Zicheng, Li, Chunyi, Wu, Haoning, Zhai, Guangtao
Recently, User-Generated Content (UGC) videos have gained popularity in our daily lives. However, UGC videos often suffer from poor exposure due to the limitations of photographic equipment and techniques. Therefore, Video Exposure Correction (VEC) a
Externí odkaz:
http://arxiv.org/abs/2405.03333
Autor:
Li, Chunyi, Wu, Haoning, Hao, Hongkun, Zhang, Zicheng, Kou, Tengchaun, Chen, Chaofeng, Bai, Lei, Liu, Xiaohong, Lin, Weisi, Zhai, Guangtao
With the evolution of Text-to-Image (T2I) models, the quality defects of AI-Generated Images (AIGIs) pose a significant barrier to their widespread adoption. In terms of both perception and alignment, existing models cannot always guarantee high-qual
Externí odkaz:
http://arxiv.org/abs/2404.18343
Autor:
Zhang, Zicheng, Wu, Haoning, Zhou, Yingjie, Li, Chunyi, Sun, Wei, Chen, Chaofeng, Min, Xiongkuo, Liu, Xiaohong, Lin, Weisi, Zhai, Guangtao
Although large multi-modality models (LMMs) have seen extensive exploration and application in various quality assessment studies, their integration into Point Cloud Quality Assessment (PCQA) remains unexplored. Given LMMs' exceptional performance an
Externí odkaz:
http://arxiv.org/abs/2404.18203
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:
Conde, Marcos V., Zadtootaghaj, Saman, Barman, Nabajeet, Timofte, Radu, He, Chenlong, Zheng, Qi, Zhu, Ruoxi, Tu, Zhengzhong, Wang, Haiqiang, Chen, Xiangguang, Meng, Wenhui, Pan, Xiang, Shi, Huiying, Zhu, Han, Xu, Xiaozhong, Sun, Lei, Chen, Zhenzhong, Liu, Shan, Zhang, Zicheng, Wu, Haoning, Zhou, Yingjie, Li, Chunyi, Liu, Xiaohong, Lin, Weisi, Zhai, Guangtao, Sun, Wei, Cao, Yuqin, Jiang, Yanwei, Jia, Jun, Zhang, Zhichao, Chen, Zijian, Zhang, Weixia, Min, Xiongkuo, Göring, Steve, Qi, Zihao, Feng, Chen
This paper reviews the AIS 2024 Video Quality Assessment (VQA) Challenge, focused on User-Generated Content (UGC). The aim of this challenge is to gather deep learning-based methods capable of estimating the perceptual quality of UGC videos. The user
Externí odkaz:
http://arxiv.org/abs/2404.16205
Autor:
Li, Chunyi, Kou, Tengchuan, Gao, Yixuan, Cao, Yuqin, Sun, Wei, Zhang, Zicheng, Zhou, Yingjie, Zhang, Zhichao, Zhang, Weixia, Wu, Haoning, Liu, Xiaohong, Min, Xiongkuo, Zhai, Guangtao
With the rapid advancements in AI-Generated Content (AIGC), AI-Generated Images (AIGIs) have been widely applied in entertainment, education, and social media. However, due to the significant variance in quality among different AIGIs, there is an urg
Externí odkaz:
http://arxiv.org/abs/2404.03407
Autor:
Kou, Tengchuan, Liu, Xiaohong, Zhang, Zicheng, Li, Chunyi, Wu, Haoning, Min, Xiongkuo, Zhai, Guangtao, Liu, Ning
With the rapid development of generative models, Artificial Intelligence-Generated Contents (AIGC) have exponentially increased in daily lives. Among them, Text-to-Video (T2V) generation has received widespread attention. Though many T2V models have
Externí odkaz:
http://arxiv.org/abs/2403.11956