# Abduction of Domain Relationships from Data for VQA

arXiv: https://arxiv.org/abs/2502.09219 · HTML: https://mlsanity.com/paper/2502.09219/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 8 references that are in the map.
- First posted on arXiv 13 Feb 2025.

## Builds on (most cited first, 6 of 6)

- [CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning](https://mlsanity.com/paper/1612.06890/) (2016)
- [OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge](https://mlsanity.com/paper/1906.00067/) (2019)
- [A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge](https://mlsanity.com/paper/2206.01718/) (2022)
- [FVQA: Fact-based Visual Question Answering](https://mlsanity.com/paper/1606.05433/) (2016)
- [A Neuro-Symbolic ASP Pipeline for Visual Question Answering](https://mlsanity.com/paper/2205.07548/) (2022)
- [Metacognitive AI: Framework and the Case for a Neurosymbolic Approach](https://mlsanity.com/paper/2406.12147/) (2024)

## 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.