Challenge: Existing methods for visual question generation focus on leveraging the semantics of inputs to propose questions, ignoring the logical coherence between generated questions and images.
Approach: They propose a logical verification method that checks logical structure between Q, images, answers and acquired outside knowledge by incorporating logical coherence between Q and Q twice in the whole procedure.
Outcome: The proposed method can generate diverse and insightful knowledge-based visual questions on two common datasets.

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Challenge: Existing methods for visual question generation use answers or question types as constraints to generate questions.
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Diversifying Question Generation over Knowledge Base via External Natural Questions (2024.lrec-main)

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Challenge: Existing methods on knowledge base question generation focus on refining the quality of a single generated question.
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Guiding Visual Question Generation (2022.naacl-main)

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Challenge: Existing approaches to Visual Question Generation (VQG) are trained to mimic an arbitrary choice of concept but only one or a few are captured by the human references.
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Challenge: Traditional visual question generation (VQG) focuses on single images, resulting in a limited ability to comprehend time-series information of the underlying event.
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Syn-QG: Syntactic and Shallow Semantic Rules for Question Generation (2020.acl-main)

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Challenge: Question Generation is a simple syntactic transformation but many aspects of semantics influence what questions are good to form.
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Challenge: Traditional supervised QG methods rely on tokenlevel alignment with fixed gold labels struggle to capture diverse valid question formulations.
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Semantic Graphs for Generating Deep Questions (2020.acl-main)

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Challenge: Existing research has focused on generating factoid questions relevant to one fact obtainable from a single sentence.
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Knowledge Image Matters: Improving Knowledge-Based Visual Reasoning with Multi-Image Large Language Models (2025.acl-long)

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Challenge: Knowledge-based visual reasoning (KB-VR) is a challenging task, as it requires machines not only to understand concepts and relationships of visual scenes, but also to associate them with external world knowledge to perform chain of reasoning on open-world questions.
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CQG: A Simple and Effective Controlled Generation Framework for Multi-hop Question Generation (2022.acl-long)

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VISREAS: Complex Visual Reasoning with Unanswerable Questions (2024.findings-acl)

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Challenge: Logic2Vision is a visual question-answering dataset that validates question authenticity with the corresponding image and then reasoning over it.
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