Xiaodong He – författare

This book addresses this challenge head-on, focusing on the intersection of groundwater sustainability and environmental health. It explores the complex relationship between groundwater management and environmental integrity, delving into topics like the impacts of groundwater extraction on ecosystems, the role of groundwater in climate change adaptation and mitigation, and the effects of surface water-groundwater exchange on groundwater quality. The book also highlights the latest scientific research, technological advancements in the field of sustainable groundwater management.

Moreover, this book not only identifies the challenges but also proposes viable solutions. It presents a range of  practices, innovative solutions, and policy recommendations to manage groundwater resources effectively while minimizing environmental impact. These will be drawn from a variety of contexts and case studies, making the book a comprehensive guide for researchers, policy makers, practitioners, and students in the field. By providing a holistic perspective on sustainable groundwater management, this book contributes significantly to our collective efforts toward achieving water security and environmental sustainability.

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 highlights the key models used in VQA.