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Computer vision and image understanding : CVIU v.163, 2017년, pp.21 - 40   SCI SCIE SCOPUS
본 등재정보는 저널의 등재정보를 참고하여 보여주는 베타서비스로 정확한 논문의 등재여부는 등재기관에 확인하시기 바랍니다.

Visual question answering: A survey of methods and datasets

Wu, Qi (Corresponding author. ) ; Teney, Damien ; Wang, Peng ; Shen, Chunhua ; Dick, Anthony ; van den Hengel, Anton ;
  • 초록  

    Abstract Visual Question Answering (VQA) is a challenging task that has received increasing attention from both the computer vision and the natural language processing communities. Given an image and a question in natural language, it requires reasoning over visual elements of the image and general knowledge to infer the correct answer. In the first part of this survey, we examine the state of the art by comparing modern approaches to the problem. We classify methods by their mechanism to connect the visual and textual modalities. In particular, we examine the common approach of combining convolutional and recurrent neural networks to map images and questions to a common feature space. We also discuss memory-augmented and modular architectures that interface with structured knowledge bases. In the second part of this survey, we review the datasets available for training and evaluating VQA systems. The various datatsets contain questions at different levels of complexity, which require different capabilities and types of reasoning. We examine in depth the question/answer pairs from the Visual Genome project, and evaluate the relevance of the structured annotations of images with scene graphs for VQA. Finally, we discuss promising future directions for the field, in particular the connection to structured knowledge bases and the use of natural language processing models. Highlights A comprehensive review of the state of the art on the emerging task of visual question answering Review the growing number of datasets, highlighting their distinct characteristics An in-depth analysis of the questions/answers provided in the recently-released Visual Genome dataset.


  • 주제어

    Visual question answering .   Natural language processing .   Knowledge bases .   Recurrent neural networks.  

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