mlsanityData to 7 Oct 2026

Straight to the Facts: Learning Knowledge Base Retrieval for Factual Visual Question Answering

arXiv 1809.01124 ↗First posted 4 Sep 2018cs.CV cs.AIcited by 30

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Abstract (arXiv)

Question answering is an important task for autonomous agents and virtual assistants alike and was shown to support the disabled in efficiently navigating an overwhelming environment. Many existing methods focus on observation-based questions, ignoring our ability to seamlessly combine observed content with general knowledge. To understand interactions with a knowledge base, a dataset has been introduced recently and keyword matching techniques were shown to yield compelling results despite being vulnerable to misconceptions due to synonyms and homographs. To address this issue, we develop a learning-based approach which goes straight to the facts via a learned embedding space. We demonstrate state-of-the-art results on the challenging recently introduced fact-based visual question answering dataset, outperforming competing methods by more than 5%.

Key facts. Cited by 30 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Sep 2018.

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Citing papers per year: 2021: 7; 2022: 9; 2023: 6; 2024: 6; 2025: 1; 2026: 1.

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  1. 2021An Empirical Study of GPT-3 for Few-Shot Knowledge-Based VQAcited by 298
  2. 2022PromptCap: Prompt-Guided Task-Aware Image Captioningcited by 104
  3. 2021Multi-Modal Answer Validation for Knowledge-Based VQAcited by 78
  4. 2024Natural Language Understanding and Inference with MLLM in Visual Question Answering: A Surveycited by 74
  5. 2022REVIVE: Regional Visual Representation Matters in Knowledge-Based Visual Question Answeringcited by 58
  6. 2021Zero-shot Visual Question Answering using Knowledge Graphcited by 37
  7. 2022A Review on Methods and Applications in Multimodal Deep Learningcited by 29
  8. 2021Recent Advances and Trends in Multimodal Deep Learning: A Reviewcited by 26
  9. 2021Reasoning over Vision and Language: Exploring the Benefits of Supplemental Knowledgecited by 24
  10. 2021Cross-Modal Retrieval Augmentation for Multi-Modal Classificationcited by 20
  11. 2022A Unified End-to-End Retriever-Reader Framework for Knowledge-based VQAcited by 18
  12. 2022Hypergraph Transformer: Weakly-supervised Multi-hop Reasoning for Knowledge-based Visual Question Answeringcited by 15
18 more
  1. 2022VQA and Visual Reasoning: An Overview of Recent Datasets, Methods and Challengescited by 14
  2. 2022A survey on knowledge-enhanced multimodal learningcited by 11
  3. 2024A Comprehensive Survey on Visual Question Answering Datasets and Algorithmscited by 8
  4. 2022Dynamic Key-value Memory Enhanced Multi-step Graph Reasoning for Knowledge-based Visual Question Answeringcited by 7
  5. 2022Semantic-aware Modular Capsule Routing for Visual Question Answeringcited by 5
  6. 2024Knowledge Generation for Zero-shot Knowledge-based VQAcited by 3
  7. 2024Diversify, Rationalize, and Combine: Ensembling Multiple QA Strategies for Zero-shot Knowledge-based VQAcited by 3
  8. 2024Visual Text Matters: Improving Text-KVQA with Visual Text Entity Knowledge-aware Large Multimodal Assistantcited by 3
  9. 2023The Contribution of Knowledge in Visiolinguistic Learning: A Survey on Tasks and Challengescited by 2
  10. 2023Multimodal Prompt Retrieval for Generative Visual Question Answeringcited by 2
  11. 2023A Survey on Interpretable Cross-modal Reasoningcited by 2
  12. 2023Multi-Clue Reasoning with Memory Augmentation for Knowledge-based Visual Question Answeringcited by 2
  13. 2026Pixel-Grounded Retrieval for Knowledgeable Large Multimodal Modelscited by 2
  14. 2023Knowledge-Enhanced Hierarchical Information Correlation Learning for Multi-Modal Rumor Detectioncited by 1
  15. 2024IIU: Independent Inference Units for Knowledge-based Visual Question Answeringcited by 1
  16. 2021EKTVQA: Generalized use of External Knowledge to empower Scene Text in Text-VQAcited by 0
  17. 2023Unveiling the frontiers of deep learning: innovations shaping diverse domainscited by 0
  18. 2025Augmented Vision-Language Models: A Systematic Reviewcited by 0