Papers with FVQA
FVQA 2.0: Introducing Adversarial Samples into Fact-based Visual Question Answering (2023.findings-eacl)
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| Challenge: | Fact-based Visual Question Answering (FVQA) is a visual question answering task that requires information retrieval using common sense knowledge graphs to answer. |
| Approach: | They propose a new test question with adversarial variants to address this imbalance by using a KB-VQA dataset that is small and contains only one answer per question. |
| Outcome: | The proposed version reduces the vulnerability of the original FVQA dataset without human annotations. |
Progressive Multimodal Search and Reasoning for Knowledge-Intensive Visual Question Answering (2026.acl-long)
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| Challenge: | Existing approaches to knowledge-intensive visual question answering lack mechanisms to revise misdirected reasoning. |
| Approach: | They propose a framework that progressively constructs a structured reasoning trajectory . they use dual-scope queries to retrieve diverse knowledge from heterogeneous knowledge bases . |
| Outcome: | The proposed framework improves retrieval recall and end-to-end answer accuracy. |