Automated Machine Learning and Meta-Learning for Multimedia

Computers & Technology

Wang, Xin

Zhu, Wenwu

Computer Science

Springer

This book explains how to use a data-driven approach to design strategies for social media content delivery. It first introduces readers to how social information can be effectively gathered for big data analysis, which provides content delivery intelligence. Secondly, the book describes data-driven models to capture information diffusion in online social networks and social media content propagation and popularity, before presenting prediction models for social media content delivery. By addressing the resource allocation and content replication aspects of social media content delivery, the book presents the latest data-driven strategies. In closing, it outlines a number of potential research directions regarding social media content delivery.

Online Social Media Content Delivery: A Data-Driven Approach (SpringerBriefs in Computer Science)

Wang, Zhi

Yang, Shiqiang

Visual Question Answering: From Theory to Application (Advances in Computer Vision and Pattern Recognition)

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.&nbsp;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, and promising 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 highlights the key models used in VQA.

Wang, Peng

He, Xiaodong

吴琦

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