TY - GEN
T1 - Change-Aware Visual Question Answering
AU - Yuan, Zhenghang
AU - Mou, Lichao
AU - Zhu, Xiao Xiang
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - aerial images
KW - change detection
KW - deep learning
KW - natural language
KW - visual question answering (VQA)
UR - http://www.scopus.com/inward/record.url?scp=85141898436&partnerID=8YFLogxK
U2 - 10.1109/IGARSS46834.2022.9884801
DO - 10.1109/IGARSS46834.2022.9884801
M3 - Conference contribution
AN - SCOPUS:85141898436
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 227
EP - 230
BT - IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022
Y2 - 17 July 2022 through 22 July 2022
ER -