Acknowledging Focus Ambiguity in Visual Questions
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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.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 42 of its 54 references that are in the map. First posted on arXiv 4 Jan 2025.
Builds on
42 of 54 references.
References · 42
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30 more
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Built on by
Citing papers per year: 2025: 1; 2026: 6.
7 papers.
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- 2026MMBench-Live: A Continuously Evolving Benchmark for Multimodal Models2 Jul 2026
- 2026GazeVLM: Active Vision via Internal Attention Control for Multimodal Reasoning8 May 2026
- 2026VLM-UQBench: A Benchmark for Modality-Specific and Cross-Modality Uncertainties in Vision Language Models9 Feb 2026
- 2026Clarify or Answer: Reinforcement Learning for Agentic VQA with Context Under-specification23 Jan 2026
- 2025Hierarchical Instance Tracking to Balance Privacy Preservation with Accessible Information10 Dec 2025