Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/116146
Citations
Scopus Web of Science® Altmetric
?
?
Type: Journal article
Title: Visual Question Answering: a tutorial
Author: Teney, D.
Wu, Q.
Van Den Hengel, A.
Citation: IEEE: Signal Processing Magazine, 2017; 34(6):63-75
Publisher: IEEE
Issue Date: 2017
ISSN: 1053-5888
1558-0792
Statement of
Responsibility: 
Damien Teney, Qi Wu, and Anton van den Hengel
Abstract: The task of visual question answering (VQA) is receiving increasing interest from researchers in both the computer vision and natural language processing fields. Tremendous advances have been seen in the field of computer vision due to the success of deep learning, in particular on low- and midlevel tasks, such as image segmentation or object recognition. These advances have fueled researchers' confidence for tackling more complex tasks that combine vision with language and high-level reasoning. VQA is a prime example of this trend. This article presents the ongoing work in the field and the current approaches to VQA based on deep learning. VQA constitutes a test for deep visual understanding and a benchmark for general artificial intelligence (AI). While the field of VQA has seen recent successes, it remains a largely unsolved task.
Rights: © 2017 IEEE
DOI: 10.1109/MSP.2017.2739826
Appears in Collections:Aurora harvest 8
Australian Institute for Machine Learning publications

Files in This Item:
File Description SizeFormat 
hdl_116146.pdfAccepted version4.61 MBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.