mlsanityData to 7 Oct 2026

Grounding Answers for Visual Questions Asked by Visually Impaired People

arXiv 2202.01993 ↗First posted 4 Feb 2022cs.CV cs.CLcited by 47

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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/.

Key facts. Cited by 47 papers in the map (data through 7 Oct 2026). Builds on 26 of its 42 references that are in the map. First posted on arXiv 4 Feb 2022.

Builds on

26 of 42 references.

References · 26

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  2. 2016Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotationscited by 2,646
  3. 2016Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answeringcited by 2,498
  4. 2015VQA: Visual Question Answeringcited by 2,489
  5. 2019Towards VQA Models That Can Readcited by 1,696
  6. 2019LXMERT: Learning Cross-Modality Encoder Representations from Transformerscited by 1,548
  7. 2020Oscar: Object-Semantics Aligned Pre-training for Vision-Language Taskscited by 1,213
  8. 2018VizWiz Grand Challenge: Answering Visual Questions from Blind Peoplecited by 852
  9. 2015Visual7W: Grounded Question Answering in Imagescited by 315
  10. 2020Counterfactual VQA: A Cause-Effect Look at Language Biascited by 266
  11. 2018Multimodal Explanations: Justifying Decisions and Pointing to the Evidencecited by 179
  12. 2016Visual Question Answering: A Survey of Methods and Datasetscited by 123
14 more
  1. 2016Human Attention in Visual Question Answering: Do Humans and Deep Networks Look at the Same Regions?cited by 111
  2. 2020Captioning Images Taken by People Who Are Blindcited by 108
  3. 2018Overcoming Language Priors in Visual Question Answering with Adversarial Regularizationcited by 104
  4. 2017An Analysis of Visual Question Answering Algorithmscited by 90
  5. 2017VQS: Linking Segmentations to Questions and Answers for Supervised Attention in VQA and Question-Focused Semantic Segmentationcited by 50
  6. 2017Exploring Human-like Attention Supervision in Visual Question Answeringcited by 36
  7. 2020Reducing Language Biases in Visual Question Answering with Visually-Grounded Question Encodercited by 34
  8. 2018Interpretable Visual Question Answering by Visual Grounding from Attention Supervision Miningcited by 28
  9. 2020Assessing Image Quality Issues for Real-World Problemscited by 28
  10. 2020AiR: Attention with Reasoning Capabilitycited by 28
  11. 2021Found a Reason for me? Weakly-supervised Grounded Visual Question Answering using Capsulescited by 22
  12. 2019Visual Question Answering using Deep Learning: A Survey and Performance Analysiscited by 19
  13. 2020Vision Skills Needed to Answer Visual Questionscited by 17
  14. 2021A First Look: Towards Explainable TextVQA Models via Visual and Textual Explanationscited by 14

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Citing papers per year: 2022: 5; 2023: 16; 2024: 12; 2025: 7; 2026: 7.

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  1. 2022Unified-IO: A Unified Model for Vision, Language, and Multi-Modal Taskscited by 420
  2. 2022Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understandingcited by 360
  3. 2022EPIC-KITCHENS VISOR Benchmark: VIdeo Segmentations and Object Relationscited by 139
  4. 2023Can I Trust Your Answer? Visually Grounded Video Question Answeringcited by 133
  5. 2023An Outlook into the Future of Egocentric Visioncited by 84
  6. 2024A Survey of Attacks on Large Vision-Language Models: Resources, Advances, and Future Trendscited by 82
  7. 2024GROUNDHOG: Grounding Large Language Models to Holistic Segmentationcited by 70
  8. 2024Toward Visual Grounding: A Surveycited by 52
  9. 2024MC-Bench: A Benchmark for Multi-Context Visual Grounding in the Era of MLLMscited by 38
  10. 2023From Image to Language: A Critical Analysis of Visual Question Answering (VQA) Approaches, Challenges, and Opportunitiescited by 24
  11. 2024OmniCount: Multi-label Object Counting with Semantic-Geometric Priorscited by 16
  12. 2023VQA Therapy: Exploring Answer Differences by Visually Grounding Answerscited by 14
35 more
  1. 2025Survey of Adversarial Robustness in Multimodal Large Language Modelscited by 14
  2. 2022VizWiz-FewShot: Locating Objects in Images Taken by People With Visual Impairmentscited by 11
  3. 2023Toloka Visual Question Answering Benchmarkcited by 10
  4. 2024Right this way: Can VLMs Guide Us to See More to Answer Questions?cited by 10
  5. 2025RefChartQA: Grounding Visual Answer on Chart Images through Instruction Tuningcited by 10
  6. 2023Dealing with Semantic Underspecification in Multimodal NLPcited by 9
  7. 2025Towards Understanding Visual Grounding in Visual Language Modelscited by 9
  8. 2023Quality-agnostic Image Captioning to Safely Assist People with Vision Impairmentcited by 8
  9. 2023Sentence Attention Blocks for Answer Groundingcited by 8
  10. 2023Salient Object Detection for Images Taken by People With Vision Impairmentscited by 7
  11. 2025Acknowledging Focus Ambiguity in Visual Questionscited by 7
  12. 2023Logical Implications for Visual Question Answering Consistencycited by 6
  13. 2024On the Role of Visual Grounding in VQAcited by 5
  14. 2024BIV-Priv-Seg: Locating Private Content in Images Taken by People With Visual Impairmentscited by 5
  15. 2024Scene-Text Grounding for Text-Based Video Question Answeringcited by 5
  16. 2023Toward Unsupervised Realistic Visual Question Answeringcited by 4
  17. 2023Interpretable Visual Question Answering via Reasoning Supervisioncited by 4
  18. 2026Instance-Aligned Captions for Explainable Video Anomaly Detectioncited by 4
  19. 2023LOIS: Looking Out of Instance Semantics for Visual Question Answeringcited by 3
  20. 2026VISTAQA: Benchmarking Joint Visual Question Answering and Pixel-Level Evidencecited by 3
  21. 2023Adaptive loose optimization for robust question answeringcited by 2
  22. 2023A Survey on Interpretable Cross-modal Reasoningcited by 2
  23. 2024Multimodal Rationales for Explainable Visual Question Answeringcited by 2
  24. 2024NaVIP: An Image-Centric Indoor Navigation Solution for Visually Impaired Peoplecited by 2
  25. 2025Situat3DChange: Situated 3D Change Understanding Dataset for Multimodal Large Language Modelcited by 2
  26. 2022Tell Me the Evidence? Dual Visual-Linguistic Interaction for Answer Groundingcited by 1
  27. 2024SimpsonsVQA: Enhancing Inquiry-Based Learning with a Tailored Datasetcited by 1
  28. 2023Knowledge-Based Counterfactual Queries for Visual Question Answeringcited by 0
  29. 2025A Framework for Generating Artificial Datasets to Validate Absolute and Relative Position Conceptscited by 0
  30. 2025Hierarchical Instance Tracking to Balance Privacy Preservation with Accessible Informationcited by 0
  31. 2026Explicit Logic Channel for Validation and Enhancement of MLLMs on Zero-Shot Taskscited by 0
  32. 2026Counterfactual Reasoning for Fine-Grained Evidence Disentanglement in VideoQAcited by 0
  33. 2026DDVT: Dynamic Dual-level Vision Transformer Fusion Network for Answer Grounding in Visual Question Answeringcited by 0
  34. 2026ENCORE: Entropy-Guided Cropping and Attention Regularization for Robust Vision--Language Understandingcited by 0
  35. 2026DocAttriBench: Benchmarking Answer Grounding in Document Visual Question Answeringcited by 0