# Answer Them All! Toward Universal Visual Question Answering Models

arXiv: https://arxiv.org/abs/1903.00366 · HTML: https://mlsanity.com/paper/1903.00366/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Mar 2019.

## Built on by (most cited first, 17 of 17)

- [CogVLM: Visual Expert for Pretrained Language Models](https://mlsanity.com/paper/2311.03079/) (2023)
- [CogCoM: A Visual Language Model with Chain-of-Manipulations Reasoning](https://mlsanity.com/paper/2402.04236/) (2024)
- [Multimodal Integration of Human-Like Attention in Visual Question Answering](https://mlsanity.com/paper/2109.13139/) (2021)
- [Towards Language-guided Visual Recognition via Dynamic Convolutions](https://mlsanity.com/paper/2110.08797/) (2021)
- [Improving Generalization in Visual Reasoning via Self-Ensemble](https://mlsanity.com/paper/2410.20883/) (2024)
- [Are Bias Mitigation Techniques for Deep Learning Effective?](https://mlsanity.com/paper/2104.00170/) (2021)
- [Cognitive Visual Commonsense Reasoning Using Dynamic Working Memory](https://mlsanity.com/paper/2107.01671/) (2021)
- [Interpretable Visual Understanding with Cognitive Attention Network](https://mlsanity.com/paper/2108.02924/) (2021)
- [QLEVR: A Diagnostic Dataset for Quantificational Language and Elementary Visual Reasoning](https://mlsanity.com/paper/2205.03075/) (2022)
- [A Picture May Be Worth a Hundred Words for Visual Question Answering](https://mlsanity.com/paper/2106.13445/) (2021)
- [LOIS: Looking Out of Instance Semantics for Visual Question Answering](https://mlsanity.com/paper/2307.14142/) (2023)
- [VolDoGer: LLM-assisted Datasets for Domain Generalization in Vision-Language Tasks](https://mlsanity.com/paper/2407.19795/) (2024)
- [SceneGATE: Scene-Graph based co-Attention networks for TExt visual question answering](https://mlsanity.com/paper/2212.08283/) (2022)
- [Improved RAMEN: Towards Domain Generalization for Visual Question Answering](https://mlsanity.com/paper/2109.02370/) (2021)
- [Question-Driven Graph Fusion Network For Visual Question Answering](https://mlsanity.com/paper/2204.00975/) (2022)
- [Attention Mechanism based Cognition-level Scene Understanding](https://mlsanity.com/paper/2204.08027/) (2022)
- [VQA with Cascade of Self- and Co-Attention Blocks](https://mlsanity.com/paper/2302.14777/) (2023)

## Abstract (arXiv)

Visual Question Answering (VQA) research is split into two camps: the first focuses on VQA datasets that require natural image understanding and the second focuses on synthetic datasets that test reasoning. A good VQA algorithm should be capable of both, but only a few VQA algorithms are tested in this manner. We compare five state-of-the-art VQA algorithms across eight VQA datasets covering both domains. To make the comparison fair, all of the models are standardized as much as possible, e.g., they use the same visual features, answer vocabularies, etc. We find that methods do not generalize across the two domains. To address this problem, we propose a new VQA algorithm that rivals or exceeds the state-of-the-art for both domains.