# Object-based reasoning in VQA

arXiv: https://arxiv.org/abs/1801.09718 · HTML: https://mlsanity.com/paper/1801.09718/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jan 2018.

## Built on by (most cited first, 7 of 7)

- [Hierarchical Object-oriented Spatio-Temporal Reasoning for Video Question Answering](https://mlsanity.com/paper/2106.13432/) (2021)
- [A Review on Methods and Applications in Multimodal Deep Learning](https://mlsanity.com/paper/2202.09195/) (2022)
- [Recent Advances and Trends in Multimodal Deep Learning: A Review](https://mlsanity.com/paper/2105.11087/) (2021)
- [Video Dialog as Conversation about Objects Living in Space-Time](https://mlsanity.com/paper/2207.03656/) (2022)
- [Object-Centric Representation Learning for Video Question Answering](https://mlsanity.com/paper/2104.05166/) (2021)
- [EPLKG: Efficient Prompt Learning with Knowledge Graph](https://mlsanity.com/paper/2304.10805/) (2023)
- [Detect2Interact: Localizing Object Key Field in Visual Question Answering (VQA) with LLMs](https://mlsanity.com/paper/2404.01151/) (2024)

## Abstract (arXiv)

Visual Question Answering (VQA) is a novel problem domain where multi-modal inputs must be processed in order to solve the task given in the form of a natural language. As the solutions inherently require to combine visual and natural language processing with abstract reasoning, the problem is considered as AI-complete. Recent advances indicate that using high-level, abstract facts extracted from the inputs might facilitate reasoning. Following that direction we decided to develop a solution combining state-of-the-art object detection and reasoning modules. The results, achieved on the well-balanced CLEVR dataset, confirm the promises and show significant, few percent improvements of accuracy on the complex "counting" task.