Self-Paced Curriculum Learning for Visual Question Answering on Remote Sensing Data

Answering questions with natural language by extracting information from image has great potential in various applications. Although visual question answering (VQA) for natural image has been broadly studied, VQA for remote sensing data is still in the early research stage. For the same remote sensing image, there exist questions with dramatically different difficulty-levels. Treating these questions equally may mislead the model and limit the VQA model performance. Considering this problem, in this work, we propose a self-paced curriculum learning (SPCL) based VQA model with hard and soft weighting strategies for remote sensing data. Like human learning process, the model is trained from easy to hard question samples gradually. Extensive experimental results on two datasets demonstrate that the proposed training method can achieve promising performance.