Zobrazeno 1 - 10
of 62
pro vyhledávání: '"Wu, Haoning"'
Soccer is a globally popular sport with a vast audience, in this paper, we consider constructing an automatic soccer game commentary model to improve the audiences' viewing experience. In general, we make the following contributions: First, observing
Externí odkaz:
http://arxiv.org/abs/2406.18530
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
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:
Zhu, Hanwei, Wu, Haoning, Li, Yixuan, Zhang, Zicheng, Chen, Baoliang, Zhu, Lingyu, Fang, Yuming, Zhai, Guangtao, Lin, Weisi, Wang, Shiqi
While recent advancements in large multimodal models (LMMs) have significantly improved their abilities in image quality assessment (IQA) relying on absolute quality rating, how to transfer reliable relative quality comparison outputs to continuous p
Externí odkaz:
http://arxiv.org/abs/2405.19298
Autor:
Sun, Wei, Wu, Haoning, Zhang, Zicheng, Jia, Jun, Zhang, Zhichao, Cao, Linhan, Chen, Qiubo, Min, Xiongkuo, Lin, Weisi, Zhai, Guangtao
In this paper, we present a simple but effective method to enhance blind video quality assessment (BVQA) models for social media videos. Motivated by previous researches that leverage pre-trained features extracted from various computer vision models
Externí odkaz:
http://arxiv.org/abs/2405.08745
Autor:
Sun, Wei, Zhang, Weixia, Jiang, Yanwei, Wu, Haoning, Zhang, Zicheng, Jia, Jun, Zhou, Yingjie, Ji, Zhongpeng, Min, Xiongkuo, Lin, Weisi, Zhai, Guangtao
Portrait images typically consist of a salient person against diverse backgrounds. With the development of mobile devices and image processing techniques, users can conveniently capture portrait images anytime and anywhere. However, the quality of th
Externí odkaz:
http://arxiv.org/abs/2405.08555
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