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Titlebook: Visual Question Answering; From Theory to Appli Qi Wu,Peng Wang,Wenwu Zhu Book 2022 The Editor(s) (if applicable) and The Author(s), under

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發(fā)表于 2025-3-21 16:26:57 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Visual Question Answering
副標(biāo)題From Theory to Appli
編輯Qi Wu,Peng Wang,Wenwu Zhu
視頻videohttp://file.papertrans.cn/984/983777/983777.mp4
概述Provides the first comprehensive survey of and handbook on visual question answering (VQA).Is self-contained and reader-friendly: ranging from basic ML and NLP concepts and theory, to details of VQA a
叢書名稱Advances in Computer Vision and Pattern Recognition
圖書封面Titlebook: Visual Question Answering; From Theory to Appli Qi Wu,Peng Wang,Wenwu Zhu Book 2022 The Editor(s) (if applicable) and The Author(s), under
描述.Visual Question Answering (VQA) usually combines visual inputs like image and video with a natural language question concerning the input and generates a natural language answer as the output.?This is by nature a multi-disciplinary research problem, involving computer vision (CV), natural language processing (NLP), knowledge representation and reasoning (KR), etc...Further, VQA is an ambitious undertaking, as it must overcome the challenges of general image understanding and the question-answering task, as well as the difficulties entailed by using large-scale databases with mixed-quality inputs. However, with the advent of deep learning (DL) and driven by the existence of advanced techniques in both CV and NLP and the availability of relevant large-scale datasets, we have recently seen enormous strides in VQA, with more systems and promising results emerging...This book provides a comprehensive overview of VQA, covering fundamental theories, models, datasets, andpromising future directions. Given its scope, it can be used as a textbook on computer vision and natural language processing, especially for researchers and students in the area of visual question answering. It also high
出版日期Book 2022
關(guān)鍵詞Visual Question Answering; VQA; Image-based Question Answering; Vision-and-Language; Deep Learning
版次1
doihttps://doi.org/10.1007/978-981-19-0964-1
isbn_softcover978-981-19-0966-5
isbn_ebook978-981-19-0964-1Series ISSN 2191-6586 Series E-ISSN 2191-6594
issn_series 2191-6586
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
The information of publication is updating

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沙發(fā)
發(fā)表于 2025-3-21 21:13:42 | 只看該作者
板凳
發(fā)表于 2025-3-22 00:46:19 | 只看該作者
Deep Learning BasicsDeep learning?basics are essential for the visual question answering task since multimodal information is usually complex and multidimensional. Therefore, in this chapter, we present basic information regarding deep learning, covering the following:
地板
發(fā)表于 2025-3-22 07:56:47 | 只看該作者
Visual Question GenerationTo explore how questions regarding images are posed and abstract the events caused by objects in the image, the visual question generation (VQG) task has been established. In this chapter, we classify VQG methods according to whether their objective is data augmentation or visual understanding.
5#
發(fā)表于 2025-3-22 08:51:12 | 只看該作者
Qi Wu,Peng Wang,Wenwu ZhuProvides the first comprehensive survey of and handbook on visual question answering (VQA).Is self-contained and reader-friendly: ranging from basic ML and NLP concepts and theory, to details of VQA a
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7#
發(fā)表于 2025-3-22 18:17:10 | 只看該作者
Advanced Models for?Video Question Answeringexist beyond this framework, which exhibit fine architectures and performances. In this chapter, we categorize these methods into four categories, i.e., ., .?and . and discuss the characteristics of these frameworks.
8#
發(fā)表于 2025-3-22 21:13:47 | 只看該作者
Advances in Computer Vision and Pattern Recognitionhttp://image.papertrans.cn/v/image/983777.jpg
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發(fā)表于 2025-3-23 01:35:46 | 只看該作者
https://doi.org/10.1007/978-981-19-0964-1Visual Question Answering; VQA; Image-based Question Answering; Vision-and-Language; Deep Learning
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發(fā)表于 2025-3-23 08:52:14 | 只看該作者
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