Questions beyond Pixels: Integrating Commonsense Knowledge in Visual Question Generation for Remote Sensing

2026-02-22

AI Summary

This work addresses the limitations of existing remote sensing visual question generation methods, which often produce overly simplistic and template-driven questions inadequate for real-world visual question answering or dialogue systems. To overcome this, the authors propose KRSVQG, a knowledge-aware model that, for the first time, incorporates external commonsense knowledge triplets into the task. The approach leverages image captions as an intermediate representation to align generated questions with visual content and employs vision-language pretraining followed by fine-tuning to perform effectively in low-data regimes. Experimental results on two newly constructed datasets, NWPU-300 and TextRS-300, demonstrate that KRSVQG significantly outperforms current methods in both automatic metrics and human evaluations, yielding questions that are more diverse and better grounded in both image semantics and domain-specific knowledge.

Technology Category

• Computer Vision: Visual Reasoning & Symbolic Representations
• Knowledge Representation and Reasoning: Common-Sense Reasoning
• Natural Language Processing: Question Answering

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web question answering
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

With the rapid development of remote sensing image archives, asking questions about images has become an effective way of gathering specific information or performing semantic image retrieval. However, current automatically generated questions tend to be simplistic and template-based, which hinders the deployment of question answering or visual dialogue systems for real-world applications. To enrich and diversify the questions with both image content and commonsense knowledge, we propose a Knowledge-aware Remote Sensing Visual Question Generation model (KRSVQG). The proposed model incorporates related knowledge triplets from external knowledge sources to broaden the question content, while employing image captioning as an intermediary representation to ground questions to the corresponding images. Moreover, KRSVQG utilizes a vision-language pre-training and fine-tuning strategy, enabling the model's adaptation to low data regimes. To evaluate the proposed KRSVQG model, we construct two knowledge-aware remote sensing visual question generation datasets: the NWPU-300 dataset and the TextRS-300 dataset. Evaluations, including metrics and human assessment, demonstrate that KRSVQG outperforms existing methods and leads to rich questions, grounded in both image and domain knowledge. As a key practice in vision-language research, knowledge-aware visual question generation advances the understanding of image content beyond pixels, facilitating the development of knowledge-enriched vision-language systems with vision-grounded human commonsense.

Problem

• Visual Question Generation
• Remote Sensing
• Commonsense Knowledge
• Knowledge Integration
• Image Understanding

Innovation

• knowledge-aware
• visual question generation
• remote sensing
• vision-language pre-training
• commonsense knowledge