{"title":"视频问题解答:最新技术调查","authors":"Jeshmol P.J., Binsu C. Kovoor","doi":"10.1016/j.jvcir.2024.104320","DOIUrl":null,"url":null,"abstract":"<div><div>Video Question Answering (VideoQA) emerges as a prominent trend in the domain of Artificial Intelligence, Computer Vision, and Natural Language Processing. It involves developing systems capable of understanding, analyzing, and responding to questions about the content of videos. The Proposed survey presents an in-depth overview of the current landscape of Question Answering, shedding light on the challenges, methodologies, datasets, and innovative approaches in the domain. The key components of the Video Question Answering (VideoQA) framework include video feature extraction, question processing, reasoning, and response generation. It underscores the importance of datasets in shaping VideoQA research and the diversity of question types, from factual inquiries to spatial and temporal reasoning. The survey highlights the ongoing research directions and future prospects for VideoQA. Finally, the proposed survey gives a road map for future explorations at the intersection of multiple disciplines, emphasizing the ultimate objective of pushing the boundaries of knowledge and innovation.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"105 ","pages":"Article 104320"},"PeriodicalIF":2.6000,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Video Question Answering: A survey of the state-of-the-art\",\"authors\":\"Jeshmol P.J., Binsu C. Kovoor\",\"doi\":\"10.1016/j.jvcir.2024.104320\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Video Question Answering (VideoQA) emerges as a prominent trend in the domain of Artificial Intelligence, Computer Vision, and Natural Language Processing. It involves developing systems capable of understanding, analyzing, and responding to questions about the content of videos. The Proposed survey presents an in-depth overview of the current landscape of Question Answering, shedding light on the challenges, methodologies, datasets, and innovative approaches in the domain. The key components of the Video Question Answering (VideoQA) framework include video feature extraction, question processing, reasoning, and response generation. It underscores the importance of datasets in shaping VideoQA research and the diversity of question types, from factual inquiries to spatial and temporal reasoning. The survey highlights the ongoing research directions and future prospects for VideoQA. Finally, the proposed survey gives a road map for future explorations at the intersection of multiple disciplines, emphasizing the ultimate objective of pushing the boundaries of knowledge and innovation.</div></div>\",\"PeriodicalId\":54755,\"journal\":{\"name\":\"Journal of Visual Communication and Image Representation\",\"volume\":\"105 \",\"pages\":\"Article 104320\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2024-10-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Visual Communication and Image Representation\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1047320324002761\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Visual Communication and Image Representation","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1047320324002761","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Video Question Answering: A survey of the state-of-the-art
Video Question Answering (VideoQA) emerges as a prominent trend in the domain of Artificial Intelligence, Computer Vision, and Natural Language Processing. It involves developing systems capable of understanding, analyzing, and responding to questions about the content of videos. The Proposed survey presents an in-depth overview of the current landscape of Question Answering, shedding light on the challenges, methodologies, datasets, and innovative approaches in the domain. The key components of the Video Question Answering (VideoQA) framework include video feature extraction, question processing, reasoning, and response generation. It underscores the importance of datasets in shaping VideoQA research and the diversity of question types, from factual inquiries to spatial and temporal reasoning. The survey highlights the ongoing research directions and future prospects for VideoQA. Finally, the proposed survey gives a road map for future explorations at the intersection of multiple disciplines, emphasizing the ultimate objective of pushing the boundaries of knowledge and innovation.
期刊介绍:
The Journal of Visual Communication and Image Representation publishes papers on state-of-the-art visual communication and image representation, with emphasis on novel technologies and theoretical work in this multidisciplinary area of pure and applied research. The field of visual communication and image representation is considered in its broadest sense and covers both digital and analog aspects as well as processing and communication in biological visual systems.