# Grounding Answers for Visual Questions Asked by Visually Impaired People

arXiv: https://arxiv.org/abs/2202.01993 · HTML: https://mlsanity.com/paper/2202.01993/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 42 references that are in the map.
- First posted on arXiv 4 Feb 2022.

## Builds on (most cited first, 26 of 26)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations](https://mlsanity.com/paper/1602.07332/) (2016)
- [Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering](https://mlsanity.com/paper/1612.00837/) (2016)
- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [Towards VQA Models That Can Read](https://mlsanity.com/paper/1904.08920/) (2019)
- [LXMERT: Learning Cross-Modality Encoder Representations from Transformers](https://mlsanity.com/paper/1908.07490/) (2019)
- [Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks](https://mlsanity.com/paper/2004.06165/) (2020)
- [VizWiz Grand Challenge: Answering Visual Questions from Blind People](https://mlsanity.com/paper/1802.08218/) (2018)
- [Visual7W: Grounded Question Answering in Images](https://mlsanity.com/paper/1511.03416/) (2015)
- [Counterfactual VQA: A Cause-Effect Look at Language Bias](https://mlsanity.com/paper/2006.04315/) (2020)
- [Multimodal Explanations: Justifying Decisions and Pointing to the Evidence](https://mlsanity.com/paper/1802.08129/) (2018)
- [Visual Question Answering: A Survey of Methods and Datasets](https://mlsanity.com/paper/1607.05910/) (2016)
- [Human Attention in Visual Question Answering: Do Humans and Deep Networks Look at the Same Regions?](https://mlsanity.com/paper/1606.03556/) (2016)
- [Captioning Images Taken by People Who Are Blind](https://mlsanity.com/paper/2002.08565/) (2020)
- [Overcoming Language Priors in Visual Question Answering with Adversarial Regularization](https://mlsanity.com/paper/1810.03649/) (2018)
- [An Analysis of Visual Question Answering Algorithms](https://mlsanity.com/paper/1703.09684/) (2017)
- [VQS: Linking Segmentations to Questions and Answers for Supervised Attention in VQA and Question-Focused Semantic Segmentation](https://mlsanity.com/paper/1708.04686/) (2017)
- [Exploring Human-like Attention Supervision in Visual Question Answering](https://mlsanity.com/paper/1709.06308/) (2017)
- [Reducing Language Biases in Visual Question Answering with Visually-Grounded Question Encoder](https://mlsanity.com/paper/2007.06198/) (2020)
- [Interpretable Visual Question Answering by Visual Grounding from Attention Supervision Mining](https://mlsanity.com/paper/1808.00265/) (2018)
- [Assessing Image Quality Issues for Real-World Problems](https://mlsanity.com/paper/2003.12511/) (2020)
- [AiR: Attention with Reasoning Capability](https://mlsanity.com/paper/2007.14419/) (2020)
- [Found a Reason for me? Weakly-supervised Grounded Visual Question Answering using Capsules](https://mlsanity.com/paper/2105.04836/) (2021)
- [Visual Question Answering using Deep Learning: A Survey and Performance Analysis](https://mlsanity.com/paper/1909.01860/) (2019)
- [Vision Skills Needed to Answer Visual Questions](https://mlsanity.com/paper/2010.03160/) (2020)
- [A First Look: Towards Explainable TextVQA Models via Visual and Textual Explanations](https://mlsanity.com/paper/2105.02626/) (2021)

## Built on by (most cited first, 30 of 47)

- [Unified-IO: A Unified Model for Vision, Language, and Multi-Modal Tasks](https://mlsanity.com/paper/2206.08916/) (2022)
- [Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding](https://mlsanity.com/paper/2210.03347/) (2022)
- [EPIC-KITCHENS VISOR Benchmark: VIdeo Segmentations and Object Relations](https://mlsanity.com/paper/2209.13064/) (2022)
- [Can I Trust Your Answer? Visually Grounded Video Question Answering](https://mlsanity.com/paper/2309.01327/) (2023)
- [An Outlook into the Future of Egocentric Vision](https://mlsanity.com/paper/2308.07123/) (2023)
- [A Survey of Attacks on Large Vision-Language Models: Resources, Advances, and Future Trends](https://mlsanity.com/paper/2407.07403/) (2024)
- [GROUNDHOG: Grounding Large Language Models to Holistic Segmentation](https://mlsanity.com/paper/2402.16846/) (2024)
- [Toward Visual Grounding: A Survey](https://mlsanity.com/paper/2412.20206/) (2024)
- [MC-Bench: A Benchmark for Multi-Context Visual Grounding in the Era of MLLMs](https://mlsanity.com/paper/2410.12332/) (2024)
- [From Image to Language: A Critical Analysis of Visual Question Answering (VQA) Approaches, Challenges, and Opportunities](https://mlsanity.com/paper/2311.00308/) (2023)
- [OmniCount: Multi-label Object Counting with Semantic-Geometric Priors](https://mlsanity.com/paper/2403.05435/) (2024)
- [Survey of Adversarial Robustness in Multimodal Large Language Models](https://mlsanity.com/paper/2503.13962/) (2025)
- [VQA Therapy: Exploring Answer Differences by Visually Grounding Answers](https://mlsanity.com/paper/2308.11662/) (2023)
- [VizWiz-FewShot: Locating Objects in Images Taken by People With Visual Impairments](https://mlsanity.com/paper/2207.11810/) (2022)
- [RefChartQA: Grounding Visual Answer on Chart Images through Instruction Tuning](https://mlsanity.com/paper/2503.23131/) (2025)
- [Toloka Visual Question Answering Benchmark](https://mlsanity.com/paper/2309.16511/) (2023)
- [Right this way: Can VLMs Guide Us to See More to Answer Questions?](https://mlsanity.com/paper/2411.00394/) (2024)
- [Towards Understanding Visual Grounding in Visual Language Models](https://mlsanity.com/paper/2509.10345/) (2025)
- [Dealing with Semantic Underspecification in Multimodal NLP](https://mlsanity.com/paper/2306.05240/) (2023)
- [Quality-agnostic Image Captioning to Safely Assist People with Vision Impairment](https://mlsanity.com/paper/2304.14623/) (2023)
- [Sentence Attention Blocks for Answer Grounding](https://mlsanity.com/paper/2309.11593/) (2023)
- [Acknowledging Focus Ambiguity in Visual Questions](https://mlsanity.com/paper/2501.02201/) (2025)
- [Salient Object Detection for Images Taken by People With Vision Impairments](https://mlsanity.com/paper/2301.05323/) (2023)
- [Logical Implications for Visual Question Answering Consistency](https://mlsanity.com/paper/2303.09427/) (2023)
- [On the Role of Visual Grounding in VQA](https://mlsanity.com/paper/2406.18253/) (2024)
- [BIV-Priv-Seg: Locating Private Content in Images Taken by People With Visual Impairments](https://mlsanity.com/paper/2407.18243/) (2024)
- [Scene-Text Grounding for Text-Based Video Question Answering](https://mlsanity.com/paper/2409.14319/) (2024)
- [Toward Unsupervised Realistic Visual Question Answering](https://mlsanity.com/paper/2303.05068/) (2023)
- [Interpretable Visual Question Answering via Reasoning Supervision](https://mlsanity.com/paper/2309.03726/) (2023)
- [Instance-Aligned Captions for Explainable Video Anomaly Detection](https://mlsanity.com/paper/2601.08155/) (2026)

## Abstract (arXiv)

Visual question answering is the task of answering questions about images. We introduce the VizWiz-VQA-Grounding dataset, the first dataset that visually grounds answers to visual questions asked by people with visual impairments. We analyze our dataset and compare it with five VQA-Grounding datasets to demonstrate what makes it similar and different. We then evaluate the SOTA VQA and VQA-Grounding models and demonstrate that current SOTA algorithms often fail to identify the correct visual evidence where the answer is located. These models regularly struggle when the visual evidence occupies a small fraction of the image, for images that are higher quality, as well as for visual questions that require skills in text recognition. The dataset, evaluation server, and leaderboard all can be found at the following link: https://vizwiz.org/tasks-and-datasets/answer-grounding-for-vqa/.