Multimodal Learning in Image Processing  

作　　者：Zhixin Chen Gautam Srivastava Shuai Liu 

机构地区：[1]School of Educational Science,Hunan Normal University,Changsha,410081,China [2]Institute of Interdisciplinary Studies,Hunan Normal University,Changsha,410081,China [3]Department of Math and Computer Science,Brandon University,Brandon,MB R7A 6A9,Canada [4]Research Centre for Interneural Computing,China Medical University,Taichung,406040,Taiwan [5]Institute of Engineering and Technology,Chitkara University,Chandigarh,140401,India

出　　处：《Computers, Materials & Continua》2025年第2期3615-3618,共4页计算机、材料和连续体(英文)

基　　金：supported by 2023 Key Supported Project of the 14th Five Year Plan for Education and Science in Hunan Province with No.XJK23AXX001;2021 Supported Project of the Educational Science Plan in Hunan Province with No.XJK21BXX010.

摘　　要：1 Introduction onMultimodal Learning in Image Processing IP(Image processing),as a classical research domain in computer application technology,has been researched for decades.It is one of the most important research directions in computer vision,which is the basis for many current hotspots such as intelligent transportation/education/industry,etc.Because image processing is the strongest link for AI(artificial intelligence)applying to real world application,it has been a challenging research field with the development of AI,from DNN(deep convolutional network),Attention/LSTM(long-short term memory),to Transformer/Diffusion/Mamba based GAI(generated AI)models,e.g.,GPT and Sora[1].Today,the description ability of single-model feature limits the performance of image processing.More comprehensive description of the image is required to match the computational performance of current large scale models.

关 键 词：IMAGE COMPUTER LSTM 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]