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Abduction of Domain Relationships from Data for VQA

arXiv 2502.09219 ↗First posted 13 Feb 2025cs.LO cs.AI cs.LGcited by 0

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Abstract (arXiv)

In this paper, we study the problem of visual question answering (VQA) where the image and query are represented by ASP programs that lack domain data. We provide an approach that is orthogonal and complementary to existing knowledge augmentation techniques where we abduce domain relationships of image constructs from past examples. After framing the abduction problem, we provide a baseline approach, and an implementation that significantly improves the accuracy of query answering yet requires few examples.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 8 references that are in the map. First posted on arXiv 13 Feb 2025.

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References · 6

  1. 2016CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoningcited by 1,445
  2. 2019OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledgecited by 1,114
  3. 2022A-OKVQA: A Benchmark for Visual Question Answering using World Knowledgecited by 702
  4. 2016FVQA: Fact-based Visual Question Answeringcited by 181
  5. 2022A Neuro-Symbolic ASP Pipeline for Visual Question Answeringcited by 14
  6. 2024Metacognitive AI: Framework and the Case for a Neurosymbolic Approachcited by 9

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