# Show, Ask, Attend, and Answer: A Strong Baseline For Visual Question Answering

arXiv: https://arxiv.org/abs/1704.03162 · HTML: https://mlsanity.com/paper/1704.03162/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2017.

## Built on by (most cited first, 30 of 31)

- [InfographicVQA](https://mlsanity.com/paper/2104.12756/) (2021)
- [Revisiting Weakly Supervised Pre-Training of Visual Perception Models](https://mlsanity.com/paper/2201.08371/) (2022)
- [MapQA: A Dataset for Question Answering on Choropleth Maps](https://mlsanity.com/paper/2211.08545/) (2022)
- [Natural Language Understanding and Inference with MLLM in Visual Question Answering: A Survey](https://mlsanity.com/paper/2411.17558/) (2024)
- [Beyond Question-Based Biases: Assessing Multimodal Shortcut Learning in Visual Question Answering](https://mlsanity.com/paper/2104.03149/) (2021)
- [Learnable Cross-modal Knowledge Distillation for Multi-modal Learning with Missing Modality](https://mlsanity.com/paper/2310.01035/) (2023)
- [EDT: Improving Large Language Models' Generation by Entropy-based Dynamic Temperature Sampling](https://mlsanity.com/paper/2403.14541/) (2024)
- [Rethinking Attention-Model Explainability through Faithfulness Violation Test](https://mlsanity.com/paper/2201.12114/) (2022)
- [Multimodal Explainable Artificial Intelligence: A Comprehensive Review of Methodological Advances and Future Research Directions](https://mlsanity.com/paper/2306.05731/) (2023)
- [From Image to Language: A Critical Analysis of Visual Question Answering (VQA) Approaches, Challenges, and Opportunities](https://mlsanity.com/paper/2311.00308/) (2023)
- [OpenViVQA: Task, Dataset, and Multimodal Fusion Models for Visual Question Answering in Vietnamese](https://mlsanity.com/paper/2305.04183/) (2023)
- [MuMuQA: Multimedia Multi-Hop News Question Answering via Cross-Media Knowledge Extraction and Grounding](https://mlsanity.com/paper/2112.10728/) (2021)
- [AdaVQA: Overcoming Language Priors with Adapted Margin Cosine Loss](https://mlsanity.com/paper/2105.01993/) (2021)
- [Robustness through Data Augmentation Loss Consistency](https://mlsanity.com/paper/2110.11205/) (2021)
- [ViOCRVQA: Novel Benchmark Dataset and Vision Reader for Visual Question Answering by Understanding Vietnamese Text in Images](https://mlsanity.com/paper/2404.18397/) (2024)
- [Correlation Information Bottleneck: Towards Adapting Pretrained Multimodal Models for Robust Visual Question Answering](https://mlsanity.com/paper/2209.06954/) (2022)
- [AdaCM2: On Understanding Extremely Long-Term Video with Adaptive Cross-Modality Memory Reduction](https://mlsanity.com/paper/2411.12593/) (2024)
- [Towards Answering Open-ended Ethical Quandary Questions](https://mlsanity.com/paper/2205.05989/) (2022)
- [PAT: Parallel Attention Transformer for Visual Question Answering in Vietnamese](https://mlsanity.com/paper/2307.08247/) (2023)
- [Story Generation from Visual Inputs: Techniques, Related Tasks, and Challenges](https://mlsanity.com/paper/2406.02748/) (2024)
- [Latent Variable Models for Visual Question Answering](https://mlsanity.com/paper/2101.06399/) (2021)
- [Generating and Evaluating Explanations of Attended and Error-Inducing Input Regions for VQA Models](https://mlsanity.com/paper/2103.14712/) (2021)
- [Show Why the Answer is Correct! Towards Explainable AI using Compositional Temporal Attention](https://mlsanity.com/paper/2105.07141/) (2021)
- [Multi-Image Visual Question Answering](https://mlsanity.com/paper/2112.13706/) (2021)
- [Medical visual question answering using joint self-supervised learning](https://mlsanity.com/paper/2302.13069/) (2023)
- [@Bench: Benchmarking Vision-Language Models for Human-centered Assistive Technology](https://mlsanity.com/paper/2409.14215/) (2024)
- [Can you even tell left from right? Presenting a new challenge for VQA](https://mlsanity.com/paper/2203.07664/) (2022)
- [VQA with Cascade of Self- and Co-Attention Blocks](https://mlsanity.com/paper/2302.14777/) (2023)
- [Knowledge-Based Counterfactual Queries for Visual Question Answering](https://mlsanity.com/paper/2303.02601/) (2023)
- [On the Cognition of Visual Question Answering Models and Human Intelligence: A Comparative Study](https://mlsanity.com/paper/2310.02528/) (2023)

## Abstract (arXiv)

This paper presents a new baseline for visual question answering task. Given an image and a question in natural language, our model produces accurate answers according to the content of the image. Our model, while being architecturally simple and relatively small in terms of trainable parameters, sets a new state of the art on both unbalanced and balanced VQA benchmark. On VQA 1.0 open ended challenge, our model achieves 64.6% accuracy on the test-standard set without using additional data, an improvement of 0.4% over state of the art, and on newly released VQA 2.0, our model scores 59.7% on validation set outperforming best previously reported results by 0.5%. The results presented in this paper are especially interesting because very similar models have been tried before but significantly lower performance were reported. In light of the new results we hope to see more meaningful research on visual question answering in the future.