Adaptive Conditional Reasoning for Remote Sensing Visual Question Answering

Remote Sensing Visual Question Answering (RS-VQA) is a research task that combines remote sensing image processing and natural language understanding. The increasing complexity and diversity of question types in Remote Sensing Visual Question Answering (RS-VQA) pose significant challenges for unified multimodal reasoning within a single model architecture. Therefore, we propose the Adaptive Conditional Reasoning (ACR) network, a novel framework that dynamically tailors reasoning pathways to question semantics through type-aware feature fusion. The ACR module selectively applies different reasoning strategies depending on whether the question is open-ended or closed-ended, thereby tailoring the reasoning process to the specific nature of the question. In order to enhance the multimodal fusion process of different types of questions, the ACR model further integrates visual and textual features by leveraging type-guided cross-attention. Meanwhile, we use a Dual-Reconstruction Feature Enhancer that mitigates spatial and channel redundancy in remote sensing images via spatial and channel reconstruction convolution, enhancing discriminative feature extraction for key regions. Experimental results demonstrate that our method achieves 78.5% overall accuracy on the EarthVQA dataset, showcasing the effectiveness of adaptive reasoning in remote sensing application.