Bøger af Wenwu Zhu

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, 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.

This book disseminates and promotes the recent research progress and frontier development on AutoML and meta-learning as well as their applications on computer vision, natural language processing, multimedia and data mining related fields.

This book explains how to use a data-driven approach to design strategies for social media content delivery. 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.

This brief presents new architecture and strategies for distribution of social video content. The book identifies the unique characteristics of socially-aware video access and social content propagation, revealing the design and integration of individual modules that are aimed at enhancing user experience in the social network context.