  Aiming at answering questions based on the content of remotely sensed images,
visual question answering for remote sensing data (RSVQA) has attracted much
attention nowadays. However, previous works in RSVQA have focused little on the
robustness of RSVQA. As we aim to enhance the reliability of RSVQA models, how
to learn robust representations against new words and different question
templates with the same meaning is the key challenge. With the proposed
augmented dataset, we are able to obtain more questions in addition to the
original ones with the same meaning. To make better use of this information, in
this study, we propose a contrastive learning strategy for training robust
RSVQA models against diverse question templates and words. Experimental results
demonstrate that the proposed augmented dataset is effective in improving the
robustness of the RSVQA model. In addition, the contrastive learning strategy
performs well on the low resolution (LR) dataset.
