Zobrazeno 1 - 10
of 5 432
pro vyhledávání: '"LIU, Xiaohong"'
Autor:
Li, Chunyi, Zhang, Jianbo, Zhang, Zicheng, Wu, Haoning, Tian, Yuan, Sun, Wei, Lu, Guo, Liu, Xiaohong, Min, Xiongkuo, Lin, Weisi, Zhai, Guangtao
The outstanding performance of Large Multimodal Models (LMMs) has made them widely applied in vision-related tasks. However, various corruptions in the real world mean that images will not be as ideal as in simulations, presenting significant challen
Externí odkaz:
http://arxiv.org/abs/2410.05474
Autor:
Liu, Kai, Zhang, Ziqing, Li, Wenbo, Pei, Renjing, Song, Fenglong, Liu, Xiaohong, Kong, Linghe, Zhang, Yulun
Image quality assessment (IQA) serves as the golden standard for all models' performance in nearly all computer vision fields. However, it still suffers from poor out-of-distribution generalization ability and expensive training costs. To address the
Externí odkaz:
http://arxiv.org/abs/2410.02505
Autor:
Zhang, Zicheng, Jia, Ziheng, Wu, Haoning, Li, Chunyi, Chen, Zijian, Zhou, Yingjie, Sun, Wei, Liu, Xiaohong, Min, Xiongkuo, Lin, Weisi, Zhai, Guangtao
With the rising interest in research on Large Multi-modal Models (LMMs) for video understanding, many studies have emphasized general video comprehension capabilities, neglecting the systematic exploration into video quality understanding. To address
Externí odkaz:
http://arxiv.org/abs/2409.20063
Autor:
Liu, Xiaohong, Yang, Guoxing, Luo, Yulin, Mao, Jiaji, Zhang, Xiang, Gao, Ming, Zhang, Shanghang, Shen, Jun, Wang, Guangyu
Radiology is a vital and complex component of modern clinical workflow and covers many tasks. Recently, vision-language (VL) foundation models in medicine have shown potential in processing multimodal information, offering a unified solution for vari
Externí odkaz:
http://arxiv.org/abs/2409.16183
Autor:
Pan, Yongyang, Liu, Xiaohong, Luo, Siqi, Xin, Yi, Guo, Xiao, Liu, Xiaoming, Min, Xiongkuo, Zhai, Guangtao
Rapid advancements in multimodal large language models have enabled the creation of hyper-realistic images from textual descriptions. However, these advancements also raise significant concerns about unauthorized use, which hinders their broader dist
Externí odkaz:
http://arxiv.org/abs/2409.10958
Autor:
Sun, Yinan, Zhang, Zicheng, Wu, Haoning, Liu, Xiaohong, Lin, Weisi, Zhai, Guangtao, Min, Xiongkuo
The rapid development of Multi-modality Large Language Models (MLLMs) has significantly influenced various aspects of industry and daily life, showcasing impressive capabilities in visual perception and understanding. However, these models also exhib
Externí odkaz:
http://arxiv.org/abs/2409.09748
Autor:
Zhou, Yingjie, Zhang, Zicheng, Wen, Farong, Jia, Jun, Jiang, Yanwei, Liu, Xiaohong, Min, Xiongkuo, Zhai, Guangtao
Although 3D generated content (3DGC) offers advantages in reducing production costs and accelerating design timelines, its quality often falls short when compared to 3D professionally generated content. Common quality issues frequently affect 3DGC, h
Externí odkaz:
http://arxiv.org/abs/2409.07236
Deep neural networks often encounter significant performance drops while facing with domain shifts between training (source) and test (target) data. To address this issue, Test Time Adaptation (TTA) methods have been proposed to adapt pre-trained sou
Externí odkaz:
http://arxiv.org/abs/2409.01341
Autor:
Han, Rong, Liu, Xiaohong, Pan, Tong, Xu, Jing, Wang, Xiaoyu, Lan, Wuyang, Li, Zhenyu, Wang, Zixuan, Song, Jiangning, Wang, Guangyu, Chen, Ting
Accurately measuring protein-RNA binding affinity is crucial in many biological processes and drug design. Previous computational methods for protein-RNA binding affinity prediction rely on either sequence or structure features, unable to capture the
Externí odkaz:
http://arxiv.org/abs/2409.03773
Quality assessment, which evaluates the visual quality level of multimedia experiences, has garnered significant attention from researchers and has evolved substantially through dedicated efforts. Before the advent of large models, quality assessment
Externí odkaz:
http://arxiv.org/abs/2409.00031