Change Detection Meets Visual Question Answering

The Earth's surface is continually changing, and identifying changes plays an important role in urban planning and sustainability. Although change detection techniques have been successfully developed for many years, these techniques are still limited to experts and facilitators in related fields. In order to provide every user with flexible access to change information and help them better understand land-cover changes, we introduce a novel task: change detection-based visual question answering (CDVQA) on multitemporal aerial images. In particular, multitemporal images can be queried to obtain high-level change-based information according to content changes between two input images. We first build a CDVQA dataset, including multitemporal image-question-answer triplets using an automatic question-answer generation method. Then, a baseline CDVQA framework is devised in this work, and it contains four parts: multitemporal feature encoding, multitemporal fusion, multimodal fusion, and answer prediction. In addition, we also introduce a change enhancing module to multitemporal feature encoding, aiming at incorporating more change-related information. Finally, the effects of different backbones and multitemporal fusion strategies are studied on the performance of CDVQA task. The experimental results provide useful insights for developing better CDVQA models, which are important for future research on this task. The dataset will be available at https://github.com/YZHJessica/CDVQA.