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pro vyhledávání: '"YAN Qiong"'
Image degradation caused by noise and blur remains a persistent challenge in imaging systems, stemming from limitations in both hardware and methodology. Single-image solutions face an inherent tradeoff between noise reduction and motion blur. While
Externí odkaz:
http://arxiv.org/abs/2412.07256
Autor:
Chen, Chaofeng, Yang, Sensen, Wu, Haoning, Liao, Liang, Zhang, Zicheng, Wang, Annan, Sun, Wenxiu, Yan, Qiong, Lin, Weisi
Recent advances of large multi-modality models (LMM) have greatly improved the ability of image quality assessment (IQA) method to evaluate and explain the quality of visual content. However, these advancements are mostly focused on overall quality a
Externí odkaz:
http://arxiv.org/abs/2407.17035
Autor:
Wu, Haoning, Zhu, Hanwei, Zhang, Zicheng, Zhang, Erli, Chen, Chaofeng, Liao, Liang, Li, Chunyi, Wang, Annan, Sun, Wenxiu, Yan, Qiong, Liu, Xiaohong, Zhai, Guangtao, Wang, Shiqi, Lin, Weisi
Comparative settings (e.g. pairwise choice, listwise ranking) have been adopted by a wide range of subjective studies for image quality assessment (IQA), as it inherently standardizes the evaluation criteria across different observers and offer more
Externí odkaz:
http://arxiv.org/abs/2402.16641
Autor:
Wu, Haoning, Zhang, Zicheng, Zhang, Weixia, Chen, Chaofeng, Liao, Liang, Li, Chunyi, Gao, Yixuan, Wang, Annan, Zhang, Erli, Sun, Wenxiu, Yan, Qiong, Min, Xiongkuo, Zhai, Guangtao, Lin, Weisi
The explosion of visual content available online underscores the requirement for an accurate machine assessor to robustly evaluate scores across diverse types of visual contents. While recent studies have demonstrated the exceptional potentials of la
Externí odkaz:
http://arxiv.org/abs/2312.17090
Autor:
Chen, Chaofeng, Zhou, Shangchen, Liao, Liang, Wu, Haoning, Sun, Wenxiu, Yan, Qiong, Lin, Weisi
Real-world image super-resolution (RWSR) is a long-standing problem as low-quality (LQ) images often have complex and unidentified degradations. Existing methods such as Generative Adversarial Networks (GANs) or continuous diffusion models present th
Externí odkaz:
http://arxiv.org/abs/2312.05616
Text-to-image diffusion models are typically trained to optimize the log-likelihood objective, which presents challenges in meeting specific requirements for downstream tasks, such as image aesthetics and image-text alignment. Recent research address
Externí odkaz:
http://arxiv.org/abs/2311.15657
Autor:
Wu, Haoning, Zhang, Zicheng, Zhang, Erli, Chen, Chaofeng, Liao, Liang, Wang, Annan, Xu, Kaixin, Li, Chunyi, Hou, Jingwen, Zhai, Guangtao, Xue, Geng, Sun, Wenxiu, Yan, Qiong, Lin, Weisi
Multi-modality foundation models, as represented by GPT-4V, have brought a new paradigm for low-level visual perception and understanding tasks, that can respond to a broad range of natural human instructions in a model. While existing foundation mod
Externí odkaz:
http://arxiv.org/abs/2311.06783
Autor:
Cheng, Yan‐Qiong1 (AUTHOR), Zhang, Ruo‐Xi1 (AUTHOR), Li, Xing‐Yuan1 (AUTHOR), Zhou, Xiao‐Ting1 (AUTHOR), Chen, Ming2 (AUTHOR), Liu, Ai‐Jun1 (AUTHOR) mrliuaijun@163.com
Publikováno v:
CNS Neuroscience & Therapeutics. Oct2024, Vol. 30 Issue 10, p1-11. 11p.
Autor:
Wu, Haoning, Zhang, Zicheng, Zhang, Erli, Chen, Chaofeng, Liao, Liang, Wang, Annan, Li, Chunyi, Sun, Wenxiu, Yan, Qiong, Zhai, Guangtao, Lin, Weisi
The rapid evolution of Multi-modality Large Language Models (MLLMs) has catalyzed a shift in computer vision from specialized models to general-purpose foundation models. Nevertheless, there is still an inadequacy in assessing the abilities of MLLMs
Externí odkaz:
http://arxiv.org/abs/2309.14181
Image Quality Assessment (IQA) constitutes a fundamental task within the field of computer vision, yet it remains an unresolved challenge, owing to the intricate distortion conditions, diverse image contents, and limited availability of data. Recentl
Externí odkaz:
http://arxiv.org/abs/2308.12001