# Simple Baseline for Visual Question Answering

arXiv: https://arxiv.org/abs/1512.02167 · HTML: https://mlsanity.com/paper/1512.02167/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2015.

## Built on by (most cited first, 24 of 24)

- [Clotho-AQA: A Crowdsourced Dataset for Audio Question Answering](https://mlsanity.com/paper/2204.09634/) (2022)
- [Natural Language Understanding and Inference with MLLM in Visual Question Answering: A Survey](https://mlsanity.com/paper/2411.17558/) (2024)
- [Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answering](https://mlsanity.com/paper/2107.02331/) (2021)
- [SwapMix: Diagnosing and Regularizing the Over-Reliance on Visual Context in Visual Question Answering](https://mlsanity.com/paper/2204.02285/) (2022)
- [Towards Explainable 3D Grounded Visual Question Answering: A New Benchmark and Strong Baseline](https://mlsanity.com/paper/2209.12028/) (2022)
- [From Image to Language: A Critical Analysis of Visual Question Answering (VQA) Approaches, Challenges, and Opportunities](https://mlsanity.com/paper/2311.00308/) (2023)
- [ProTo: Program-Guided Transformer for Program-Guided Tasks](https://mlsanity.com/paper/2110.00804/) (2021)
- [LOVA3: Learning to Visual Question Answering, Asking and Assessment](https://mlsanity.com/paper/2405.14974/) (2024)
- [VQA-Aid: Visual Question Answering for Post-Disaster Damage Assessment and Analysis](https://mlsanity.com/paper/2106.10548/) (2021)
- [VQA and Visual Reasoning: An Overview of Recent Datasets, Methods and Challenges](https://mlsanity.com/paper/2212.13296/) (2022)
- [Exploring Phrase-Level Grounding with Text-to-Image Diffusion Model](https://mlsanity.com/paper/2407.05352/) (2024)
- [Experimental Standards for Deep Learning in Natural Language Processing Research](https://mlsanity.com/paper/2204.06251/) (2022)
- [MuVAM: A Multi-View Attention-based Model for Medical Visual Question Answering](https://mlsanity.com/paper/2107.03216/) (2021)
- [EAML: Ensemble Self-Attention-based Mutual Learning Network for Document Image Classification](https://mlsanity.com/paper/2305.06923/) (2023)
- [Semi-Supervised Panoptic Narrative Grounding](https://mlsanity.com/paper/2310.18142/) (2023)
- [Show Why the Answer is Correct! Towards Explainable AI using Compositional Temporal Attention](https://mlsanity.com/paper/2105.07141/) (2021)
- [MGA-VQA: Multi-Granularity Alignment for Visual Question Answering](https://mlsanity.com/paper/2201.10656/) (2022)
- [A Better Loss for Visual-Textual Grounding](https://mlsanity.com/paper/2108.05308/) (2021)
- [Bilaterally Slimmable Transformer for Elastic and Efficient Visual Question Answering](https://mlsanity.com/paper/2203.12814/) (2022)
- [BinaryVQA: A Versatile Test Set to Evaluate the Out-of-Distribution Generalization of VQA Models](https://mlsanity.com/paper/2301.12032/) (2023)
- [Dynamic Task and Weight Prioritization Curriculum Learning for Multimodal Imagery](https://mlsanity.com/paper/2310.19109/) (2023)
- [Will Multi-modal Data Improves Few-shot Learning?](https://mlsanity.com/paper/2107.11853/) (2021)
- [Deep Neural Networks for Visual Reasoning](https://mlsanity.com/paper/2209.11990/) (2022)
- [Bridge Damage Cause Estimation Using Multiple Images Based on Visual Question Answering](https://mlsanity.com/paper/2302.09208/) (2023)

## Abstract (arXiv)

We describe a very simple bag-of-words baseline for visual question answering. This baseline concatenates the word features from the question and CNN features from the image to predict the answer. When evaluated on the challenging VQA dataset [2], it shows comparable performance to many recent approaches using recurrent neural networks. To explore the strength and weakness of the trained model, we also provide an interactive web demo and open-source code. .