# Acknowledging Focus Ambiguity in Visual Questions

arXiv: https://arxiv.org/abs/2501.02201 · HTML: https://mlsanity.com/paper/2501.02201/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 42 of its 54 references that are in the map.
- First posted on arXiv 4 Jan 2025.

## Builds on (most cited first, 30 of 42)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [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)
- [MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI](https://mlsanity.com/paper/2311.16502/) (2023)
- [Towards VQA Models That Can Read](https://mlsanity.com/paper/1904.08920/) (2019)
- [MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts](https://mlsanity.com/paper/2310.02255/) (2023)
- [DocVQA: A Dataset for VQA on Document Images](https://mlsanity.com/paper/2007.00398/) (2020)
- [LVIS: A Dataset for Large Vocabulary Instance Segmentation](https://mlsanity.com/paper/1908.03195/) (2019)
- [Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference](https://mlsanity.com/paper/2403.04132/) (2024)
- [How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites](https://mlsanity.com/paper/2404.16821/) (2024)
- [SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension](https://mlsanity.com/paper/2307.16125/) (2023)
- [VizWiz Grand Challenge: Answering Visual Questions from Blind People](https://mlsanity.com/paper/1802.08218/) (2018)
- [Multimodal Few-Shot Learning with Frozen Language Models](https://mlsanity.com/paper/2106.13884/) (2021)
- [Object Detection in 20 Years: A Survey](https://mlsanity.com/paper/1905.05055/) (2019)
- [GLaMM: Pixel Grounding Large Multimodal Model](https://mlsanity.com/paper/2311.03356/) (2023)
- [AmbigQA: Answering Ambiguous Open-domain Questions](https://mlsanity.com/paper/2004.10645/) (2020)
- [Visual7W: Grounded Question Answering in Images](https://mlsanity.com/paper/1511.03416/) (2015)
- [PACO: Parts and Attributes of Common Objects](https://mlsanity.com/paper/2301.01795/) (2023)
- [PhraseCut: Language-based Image Segmentation in the Wild](https://mlsanity.com/paper/2008.01187/) (2020)
- [Salient Object Detection: A Discriminative Regional Feature Integration Approach](https://mlsanity.com/paper/1410.5926/) (2014)
- [A Survey on Instance Segmentation: State of the art](https://mlsanity.com/paper/2007.00047/) (2020)
- [Referring Expression Comprehension: A Survey of Methods and Datasets](https://mlsanity.com/paper/2007.09554/) (2020)
- [VQS: Linking Segmentations to Questions and Answers for Supervised Attention in VQA and Question-Focused Semantic Segmentation](https://mlsanity.com/paper/1708.04686/) (2017)
- [Cops-Ref: A new Dataset and Task on Compositional Referring Expression Comprehension](https://mlsanity.com/paper/2003.00403/) (2020)
- [Grounding Answers for Visual Questions Asked by Visually Impaired People](https://mlsanity.com/paper/2202.01993/) (2022)
- [Tree of Clarifications: Answering Ambiguous Questions with Retrieval-Augmented Large Language Models](https://mlsanity.com/paper/2310.14696/) (2023)
- [Described Object Detection: Liberating Object Detection with Flexible Expressions](https://mlsanity.com/paper/2307.12813/) (2023)

## Built on by (most cited first, 8 of 8)

- [Clarify or Answer: Reinforcement Learning for Agentic VQA with Context Under-specification](https://mlsanity.com/paper/2601.16400/) (2026)
- [VLM-UQBench: A Benchmark for Modality-Specific and Cross-Modality Uncertainties in Vision Language Models](https://mlsanity.com/paper/2602.09214/) (2026)
- [Hierarchical Instance Tracking to Balance Privacy Preservation with Accessible Information](https://mlsanity.com/paper/2512.10102/) (2025)
- [GazeVLM: Active Vision via Internal Attention Control for Multimodal Reasoning](https://mlsanity.com/paper/2605.07817/) (2026)
- [MMBench-Live: A Continuously Evolving Benchmark for Multimodal Models](https://mlsanity.com/paper/2607.01813/) (2026)
- [Estimating Uncertainty in Galaxy Morphology Classification](https://mlsanity.com/paper/2608.08398/) (2026)
- [COMIC: Reference-Aware Safety Gating for Multimodal Large Language Models](https://mlsanity.com/paper/2608.17234/) (2026)
- [Recognizing Dialogue Ambiguity via Candidate Set Visual Grounding](https://mlsanity.com/paper/2610.19723/) (2026)

## Abstract (arXiv)

No published work on visual question answering (VQA) accounts for ambiguity regarding where the content described in the question is located in the image. To fill this gap, we introduce VQ-FocusAmbiguity, the first VQA dataset that visually grounds each plausible image region a question could refer to when arriving at valid answers. We next analyze and compare our dataset to existing datasets to reveal its unique properties. Finally, we benchmark modern models for two novel tasks related to acknowledging focus ambiguity: recognizing whether a visual question has focus ambiguity and locating all plausible focus regions within the image. Results show that the dataset is challenging for modern models. To facilitate future progress on these tasks, we publicly share the dataset with an evaluation server at https://vizwiz.org/tasks-and-datasets/focus-ambiguity-in-visual-questions.