Change Detection Meets Visual Question Answering

Abstract

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 multi-temporal aerial images. In particular, multi-temporal 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 multi-temporal 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: multi-temporal feature encoding, multi-temporal fusion, multi-modal fusion, and answer prediction. In addition, we also introduce a change enhancing module to multi-temporal feature encoding, aiming at incorporating more change-related information. Finally, effects of different backbones and multi-temporal 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.

References in corpus (8)

• Exploring Models and Data for Remote Sensing Image Caption Generation
• GETNET: A General End-to-end Two-dimensional CNN Framework for Hyperspectral Image Change Detection
• State-of-the-Art Augmented NLP Transformer models for direct and single-step retrosynthesis
• Unsupervised Deep Slow Feature Analysis for Change Detection in Multi-Temporal Remote Sensing Images
• RSVQA: Visual Question Answering for Remote Sensing Data
• Bi-Temporal Semantic Reasoning for the Semantic Change Detection in HR Remote Sensing Images
• From Easy to Hard: Learning Language-guided Curriculum for Visual Question Answering on Remote Sensing Data
• How to find a good image-text embedding for remote sensing visual question answering?

Cited by in corpus (4)

• RSVG: Exploring Data and Models for Visual Grounding on Remote Sensing Data
• From Image to Language: A Critical Analysis of Visual Question Answering (VQA) Approaches, Challenges, and Opportunities
• Remote Sensing SpatioTemporal Vision-Language Models: A Comprehensive Survey
• Data-Driven Vision-Language Models for Remote Sensing: A Survey