Paper Info
Title
Change-Aware Visual Question Answering
Abstract
Change detection has been a hot research topic in the field of remote sensing, and it can provide information on observing changes of Earth's surface. However, segmentation-based change results are not very friendly to end users. Thus, in order to improve user experience and offer them high-level semantic information on change detection, we introduce a new task: change-aware visual question answering (VQA) on multi-temporal aerial images. Specifically, given a pair of multi-temporal aerial images and questions, this task aims to automatically provide natural language answers. By doing so, end users have better access to easy-to-understand change information through natural language. Besides, we also create a dataset made of multi-temporal image-question-answer triplets and a baseline method for this task. Experimental results offer valuable insights for the further research on this task.
Year
DOI
Venue
2022
10.1109/IGARSS46834.2022.9884801
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
Keywords
DocType
ISSN
visual question answering (VQA),change detection,aerial images,natural language,deep learning
Conference
2153-6996
ISBN
Citations 
PageRank 
978-1-6654-2793-7
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Zhenghang Yuan100.34
Lichao Mou225425.35
Xiao Xiang Zhu301.35