Zobrazeno 1 - 10
of 5 526
pro vyhledávání: '"Liu, XiaoHong"'
Autor:
Jia, Ziheng, Zhang, Zicheng, Qian, Jiaying, Wu, Haoning, Sun, Wei, Li, Chunyi, Liu, Xiaohong, Lin, Weisi, Zhai, Guangtao, Min, Xiongkuo
The advent and proliferation of large multi-modal models (LMMs) have introduced a new paradigm to video-related computer vision fields, including training and inference methods based on visual question answering (VQA). These methods enable models to
Externí odkaz:
http://arxiv.org/abs/2411.03795
Autor:
Song, Xiufeng, Guo, Xiao, Zhang, Jiache, Li, Qirui, Bai, Lei, Liu, Xiaoming, Zhai, Guangtao, Liu, Xiaohong
Large numbers of synthesized videos from diffusion models pose threats to information security and authenticity, leading to an increasing demand for generated content detection. However, existing video-level detection algorithms primarily focus on de
Externí odkaz:
http://arxiv.org/abs/2410.23623
Differences in forgery attributes of images generated in CNN-synthesized and image-editing domains are large, and such differences make a unified image forgery detection and localization (IFDL) challenging. To this end, we present a hierarchical fine
Externí odkaz:
http://arxiv.org/abs/2410.23556
Exposure Correction (EC) aims to recover proper exposure conditions for images captured under over-exposure or under-exposure scenarios. While existing deep learning models have shown promising results, few have fully embedded Retinex theory into the
Externí odkaz:
http://arxiv.org/abs/2410.21535
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