Inferential and Commonsense Visual Question Generation

Chao Bi, Shuhui Wang, Na Li, Qingming Huang

Abstract: The Visual Question Generation (VQG) task generally aims to produce questions based on images in natural language. Existing studies often handle VQG as a reverse Visual Question Answering (VQA), training data-driven generators on VQA datasets. However, this solution pipeline struggles to generate high-quality questions that effectively challenge robots and humans, even by leveraging the most advanced large-scale foundational models. There are also some other VQG methods depending on elaborate and costly manual preprocessing heavily. To address these limitations, we propose a novel method with a two-module framework for automatically generating inferential visual questions that also follow commonsense. The “Scene Graph Generation” module constructs specialized scene graphs by progressively expanding connections from high-confidence nodes. This module ensures semantic consistency by aligning visual, textual, and salient features. Additionally, we incorporate external knowledge to extend abstract semantic concepts and associated facts, enriching the content of generated questions and facilitating the generated question to better follow the commonsense of human. Another module “Question Generation” utilizes the above scene graph as a foundation to search and instantiate for the question. The generated questions will match with the program templates and have diverse inferential paths. Experimental results demonstrate that our method is both effective and highly scalable. The generated questions are controllable in terms of semantic richness and difficulty, exhibiting clear inferential and commonsense properties. Furthermore, we automatically utilize our method to create a large-scale dataset, ICVQA, which includes approximately 160,000 images and 800,000 questionanswer pairs, thereby facilitating further research in VQA and visual dialogue.

External IDs:dblp:journals/tmm/BiWLH25