Bøker i Advances in Computer Vision an-serien

With recent advances in multimedia technology, on-line libraries of digital images are assuming an ever increasing relevance within a wide range of information systems. Effective access to these archives requires not only conventional searching techniques based on keywords, but also content-based queries which allow visual features of data to be searched. This new comprehensive reference introduces the intricate science of implementing image modeling and retrieval based on the shape, on the color and texture, or on the spatial arrangement of imaged objects. Building on this introduction, six chapters by major research groups in the field detail and compare different modeling approaches.

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.