Knowledge-aware Visual Question Generation for Remote Sensing Images
This work addresses the need for more diverse and contextually grounded questions in remote sensing image analysis, which is incremental as it builds on existing visual question generation methods by adding knowledge integration.
The paper tackles the problem of simplistic and template-based question generation for remote sensing images by proposing KRSVQG, a knowledge-aware model that incorporates external knowledge and image captioning to generate enriched questions, and it outperforms existing methods on manually annotated datasets NWPU-300 and TextRS-300.
With the rapid development of remote sensing image archives, asking questions about images has become an effective way of gathering specific information or performing image retrieval. However, automatically generated image-based questions tend to be simplistic and template-based, which hinders the real deployment of question answering or visual dialogue systems. To enrich and diversify the questions, we propose a knowledge-aware remote sensing visual question generation model, KRSVQG, that incorporates external knowledge related to the image content to improve the quality and contextual understanding of the generated questions. The model takes an image and a related knowledge triplet from external knowledge sources as inputs and leverages image captioning as an intermediary representation to enhance the image grounding of the generated questions. To assess the performance of KRSVQG, we utilized two datasets that we manually annotated: NWPU-300 and TextRS-300. Results on these two datasets demonstrate that KRSVQG outperforms existing methods and leads to knowledge-enriched questions, grounded in both image and domain knowledge.