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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Video Representation Learningion understanding in videos and video question answering. Video representations can be categorized into handcrafted local features and deep-learned features. Handcrafted local features are video features extracted by handcrafted formulas, and deep-learned features are extracted automatically through

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Certainty

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.

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Embodied VQAquested. Several sub-tasks are proposed to achieve this goal in sequential manner, e.g. Vision-and-Language Navigation requires the intelligent agent to follow detailed instructions with visual perception, Remote object localization?gives the agent shorter and more abstract instructions, Embodied QA

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Text-Based VQA Texts that can be recognized by optical character recognition (OCR) tools provide considerably more useful and high-level semantic information, such as the street name, product brand and prices, which is not available in any other forms in the scene. Interpreting this written information in human e

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Visual Dialogueestions and histories to answer questions. To accomplish this task, the machine must exhibit the abilities of perception, multimodal reasoning, relationship mining and visual coreference resolution. In this chapter, we briefly describe the challenges associated with this method and introduce the two

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Working-Memory

Book 2022es 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 g

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