CHANGE-AWARE VISUAL QUESTION ANSWERING

被引：1

作者：

Yuan, Zhenghang ^{[1
]}

Mou, Lichao ^{[1
,2
]}

Zhu, Xiao Xiang ^{[1
,2
]}

机构：

[1] Tech Univ Munich TUM, Data Sci Earth Observat, Munich, Germany

[2] German Aerosp Ctr DLR, Remote Sensing Technol Inst IMF, Wessling, Germany

来源：

2022 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2022) | 2022年

关键词：

visual question answering (VQA); change detection; aerial images; natural language; deep learning;

D O I：

10.1109/IGARSS46834.2022.9884801

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

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.

引用

页码：227 / 230

页数：4

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