[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118718-en":3,"doc-seo-118718-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118718,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","FOUNDATIONS & RECENT TRENDS IN MULTIMODAL MACHINE LEARNING - PRINCIPLES, CHALLENGES, & OPEN QUESTIONS","Multimodal machine learning targets computer agents that understand, reason, and learn by integrating diverse communicative modalities such as linguistic, acoustic, visual, tactile, and physiological signals. Growing emphasis on video understanding, embodied autonomous agents, text-to-image generation, and multisensor fusion—especially in domains like healthcare and robotics—creates computational and theoretical difficulties stemming from heterogeneous data sources and cross-modal interdependencies. This paper synthesizes historical and recent work to outline foundations, two key principles of modality heterogeneity and interconnections, a taxonomy of six core challenges, and motivates open problems for future research.","arXiv :2209 .03430v 1 [ cs .LG] 7 Sep 2022  \nFOUNDATIONS & RECENT TRENDS IN MULTIMODAL MACHINE LEARNING: PRINCIPLES, CHALLENGES, & OPEN QUESTIONS  \nPaul Pu Liang, Amir Zadeh, Louis-Philippe Morency  \nLanguage Technologies Institute & Machine Learning Department  \nCarnegie Mellon University  \nPittsburgh, PA 15213  \n{pliang,abagherz,[morency}@cs.cmu.edu](morency}@cs.cmu.edu)  \nAbstract  \nMultimodal machine learning is a vibrant multi-disciplinary research field that aims to design computer agents with intelligent capabilities such as understanding, reasoning, and learning through integrating multiple communicative modalities, including linguistic, acoustic, visual, tactile, and physiological messages. With the recent interest in video understanding, embodied autonomous agents, text-to-image generation, and multisensor fusion in application domains such as healthcare and robotics, multimodal machine learning has brought unique computational and theoretical challenges to the machine learning community given the heterogeneity of data sources and the interconnections often found between modalities. However, the breadth of progress in multimodal research has made it difficult to identify the common themes and open questions in the field. By synthesizing a broad range of application domains and theoretical frameworks from both historical and recent perspectives, this paper is designed to provide an overview of the computational and theoretical foundations of multimodal machine learning. We start by defining two key principles of modality heterogeneity and interconnections that have driven subsequent innovations, and propose a taxonomy of 6 core technical challenges: representation, alignment, reasoning, generation, transference, and quantification covering historical and recent trends. Recent technical achievements will be presented through the lens of this taxonomy, allowing researchers to understand the similarities and differences across new approaches. We end by motivating several open problems for future research as identified by our taxonomy.  \n1 Introduction  \nIt has always been a grand goal of artificial intelligence to develop computer agents with intelligent capabilities such as understanding, reasoning, and learning through multimodal experiences and data, in a similar way to how we humans perceive our world using multiple sensory modalities. With recent advances in embodied autonomous agents [77, 512], self-driving cars [647], image and video understanding [16, 482, 557], text-to-image generation [486], and multisensor fusion in application domain such as robotics [335, 493] and healthcare [281, 357], we are now closer than ever to intelligent agents that can integrate and learn from many sensory modalities. This vibrant multi-disciplinary research field of multimodal machine learning brings unique challenges given the heterogeneity of the data and the interconnections often found between modalities, and has widespread applications in multimedia [351, 435], affective computing [353, 476], robotics [308, 334], human-computer interaction [445, 519], and healthcare [85, 425] .  \nHowever, the rate of progress in multimodal research has made it difficult to identify the common themes underlying historical and recent work, as well as the key open questions in the field. By synthesizing a broad range of application domains and theoretical insights from both historical and recent perspectives, this paper is designed to provide an overview of the methodological, computational, and theoretical foundations of multimodal machine learning, which nicely complements several recent application-oriented surveys in vision and language [603], language and reinforcement learning [382], multimedia analysis [40], and human-computer interaction [269] .  \nTo build up the foundations of multimodal machine learning, we begin by laying the groundwork for definitions of data modalities and multimodal research, before identifying two key principles that have dri","cbCaigp2KgZmo3Gi","https://ap.wps.com/l/cbCaigp2KgZmo3Gi","pdf",9306788,1,65,"English","en",105,"# Introduction\n## Principles and definitions\n## Taxonomy of core challenges\n## Open problems and future research","[{\"question\":\"What problem does multimodal machine learning aim to solve?\",\"answer\":\"It designs computer agents that can understand, reason, and learn by integrating information from multiple communicative modalities such as language, audio, and visual signals.\"},{\"question\":\"Which two principles motivate the paper’s technical challenges?\",\"answer\":\"The paper emphasizes modality heterogeneity and modality interconnections, explaining how diverse information qualities and relationships across modalities drive subsequent innovations.\"},{\"question\":\"What are the six core technical challenges in the proposed taxonomy?\",\"answer\":\"Representation, alignment, reasoning, generation, transference, and quantification are presented as six central challenges spanning both historical and recent trends.\"}]","FOUNDATIONS & RECENT TRENDS IN MULTIMODAL MACHINE LEARNING - 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