Zobrazeno 1 - 10
of 284 679
pro vyhledávání: '"COMPREHENSIVE SURVEY"'
Autor:
Li, Haitao, Dong, Qian, Chen, Junjie, Su, Huixue, Zhou, Yujia, Ai, Qingyao, Ye, Ziyi, Liu, Yiqun
The rapid advancement of Large Language Models (LLMs) has driven their expanding application across various fields. One of the most promising applications is their role as evaluators based on natural language responses, referred to as ''LLMs-as-judge
Externí odkaz:
http://arxiv.org/abs/2412.05579
Autor:
Zheng, Qi, Fan, Yibo, Huang, Leilei, Zhu, Tianyu, Liu, Jiaming, Hao, Zhijian, Xing, Shuo, Chen, Chia-Ju, Min, Xiongkuo, Bovik, Alan C., Tu, Zhengzhong
Video quality assessment (VQA) is an important processing task, aiming at predicting the quality of videos in a manner highly consistent with human judgments of perceived quality. Traditional VQA models based on natural image and/or video statistics,
Externí odkaz:
http://arxiv.org/abs/2412.04508
Autor:
Dang, Yunkai, Huang, Kaichen, Huo, Jiahao, Yan, Yibo, Huang, Sirui, Liu, Dongrui, Gao, Mengxi, Zhang, Jie, Qian, Chen, Wang, Kun, Liu, Yong, Shao, Jing, Xiong, Hui, Hu, Xuming
The rapid development of Artificial Intelligence (AI) has revolutionized numerous fields, with large language models (LLMs) and computer vision (CV) systems driving advancements in natural language understanding and visual processing, respectively. T
Externí odkaz:
http://arxiv.org/abs/2412.02104
Autor:
Neupane, Madhav
This comprehensive survey paper provides an in-depth analysis of Dynamic Software Updating (DSU) techniques in the Internet of Things (IoT). This study critically examines eight significant research papers that employ diverse methodologies to address
Externí odkaz:
http://arxiv.org/abs/2412.02450
Industrial networks are undergoing rapid transformation driven by the convergence of emerging technologies that are revolutionizing conventional workflows, enhancing operational efficiency, and fundamentally redefining the industrial landscape across
Externí odkaz:
http://arxiv.org/abs/2412.00209
Reinforcement Learning (RL) has emerged as a powerful paradigm in Artificial Intelligence (AI), enabling agents to learn optimal behaviors through interactions with their environments. Drawing from the foundations of trial and error, RL equips agents
Externí odkaz:
http://arxiv.org/abs/2411.18892
In recent years, deepfakes (DFs) have been utilized for malicious purposes, such as individual impersonation, misinformation spreading, and artists' style imitation, raising questions about ethical and security concerns. However, existing surveys hav
Externí odkaz:
http://arxiv.org/abs/2411.17911
Autor:
Fu, Chaoyou, Zhang, Yi-Fan, Yin, Shukang, Li, Bo, Fang, Xinyu, Zhao, Sirui, Duan, Haodong, Sun, Xing, Liu, Ziwei, Wang, Liang, Shan, Caifeng, He, Ran
As a prominent direction of Artificial General Intelligence (AGI), Multimodal Large Language Models (MLLMs) have garnered increased attention from both industry and academia. Building upon pre-trained LLMs, this family of models further develops mult
Externí odkaz:
http://arxiv.org/abs/2411.15296
Prescriptive Analytics (PSA), an emerging business analytics field suggesting concrete options for solving business problems, has seen an increasing amount of interest after more than a decade of multidisciplinary research. This paper is a comprehens
Externí odkaz:
http://arxiv.org/abs/2412.00034
Edge-AI, the convergence of edge computing and artificial intelligence (AI), has become a promising paradigm that enables the deployment of advanced AI models at the network edge, close to users. In Edge-AI, federated continual learning (FCL) has eme
Externí odkaz:
http://arxiv.org/abs/2411.13740