  Visual question answering (VQA) for remote sensing scene has great potential
in intelligent human-computer interaction system. Although VQA in computer
vision has been widely researched, VQA for remote sensing data (RSVQA) is still
in its infancy. There are two characteristics that need to be specially
considered for the RSVQA task. 1) No object annotations are available in RSVQA
datasets, which makes it difficult for models to exploit informative region
representation; 2) There are questions with clearly different difficulty levels
for each image in the RSVQA task. Directly training a model with questions in a
random order may confuse the model and limit the performance. To address these
two problems, in this paper, a multi-level visual feature learning method is
proposed to jointly extract language-guided holistic and regional image
features. Besides, a self-paced curriculum learning (SPCL)-based VQA model is
developed to train networks with samples in an easy-to-hard way. To be more
specific, a language-guided SPCL method with a soft weighting strategy is
explored in this work. The proposed model is evaluated on three public
datasets, and extensive experimental results show that the proposed RSVQA
framework can achieve promising performance.
