| Challenge: | Lack of visual grounding (VG) in VQA systems can manifest in over-reliance on irrelevant image parts or a disregard for the visual modality entirely. |
| Approach: | They propose a new metric that measures if a model identifies question-relevant objects in the scene and relies on the information contained in the relevant objects when producing its answer. |
| Outcome: | The proposed metric measures if a model identifies question-relevant objects in the scene and relies on the information contained in the relevant objects when producing its answer. |
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Uncovering the Full Potential of Visual Grounding Methods in VQA (2024.acl-long)
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| Challenge: | Visual Grounding (VG) methods in VQA aim to strengthen a model's reliance on question-relevant visual information. |
| Approach: | They propose to strengthen a model's reliance on question-relevant visual information by using a visual grounding method that is based on a question-related visual input. |
| Outcome: | The proposed methods can be much more effective when evaluation conditions are corrected. |
A negative case analysis of visual grounding methods for VQA (2020.acl-main)
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| Challenge: | Existing Visual Question Answering (VQA) methods exploit dataset biases and spurious statistical correlations instead of producing correct answers for the right reasons. |
| Approach: | They propose to incorporate visual cues to better ground VQA models . they also propose a regularization effect which prevents over-fitting to linguistic priors . |
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CommVQA: Situating Visual Question Answering in Communicative Contexts (2024.emnlp-main)
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| Challenge: | Current visual question answering models are trained on image-question pairs in isolation, but the questions people ask are dependent on their informational needs and prior knowledge about the image content. |
| Approach: | They propose a visual question-answer-as-question dataset that contains 1000 images and 8,949 question-announcer pairs to evaluate how situating images within naturalistic contexts shapes visual questions. |
| Outcome: | The proposed dataset contains 1000 images and 8,949 question-answer pairs. |
Where is this coming from? Making groundedness count in the evaluation of Document VQA models (2025.findings-naacl)
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| Challenge: | Document Visual Question Answering (VQA) models have come close to or matching human performance on some benchmarks. |
| Approach: | They propose a method that accounts for the semantic and multimodal groundedness of a model’s outputs and can be parameterized so that users can configure the score according to their preferences. |
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‘Just because you are right, doesn’t mean I am wrong’: Overcoming a bottleneck in development and evaluation of Open-Ended VQA tasks (2021.eacl-main)
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Man Luo, Shailaja Keyur Sampat, Riley Tallman, Yankai Zeng, Manuha Vancha, Akarshan Sajja, Chitta Baral
| Challenge: | Existing visual question answering datasets assume only one ground truth answer for each question. |
| Approach: | They propose alternative answer sets (AAS) of ground-truth answers to address this limitation . they modify top VQA solvers to support multiple plausible answers for a question . |
| Outcome: | The proposed approach improves on the GQA dataset and shows that it is more efficient than previous approaches. |
ProtoVQA: An Adaptable Prototypical Framework for Explainable Fine-Grained Visual Question Answering (2025.emnlp-main)
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| Challenge: | Visual Question Answering (VQA) is increasingly used in diverse applications where models must provide accurate answers and explanations that humans can easily understand and verify. |
| Approach: | They propose a unified prototypical framework that learns question-aware prototypes that serve as reasoning anchors and applies spatially constrained matching to ensure that the selected evidence is coherent and semantically relevant. |
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Sunny and Dark Outside?! Improving Answer Consistency in VQA through Entailed Question Generation (D19-1)
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| Challenge: | interacting with a model for Visual Question Answering (VQA) quickly reveals that these models lack consistency. |
| Approach: | They propose a dataset, ConVQA, and metrics that enable quantitative evaluation of consistency in VQA. |
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MAGIC-VQA: Multimodal And Grounded Inference with Commonsense Knowledge for Visual Question Answering (2025.findings-acl)
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| Challenge: | Existing Large Vision-Language Models (LVLMs) lack integrated commonsense knowledge . lack of integrated common knowledge limits their robustness and accuracy in VQA . |
| Approach: | They propose a framework to enhance multimodal inference by integrating commonsense reasoning. |
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Selectively Answering Visual Questions (2024.findings-acl)
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| Challenge: | Large multi-modal models (LMMs) are capable of visual question answering (VQA) with unprecedented accuracy. |
| Approach: | They propose a calibration score that can be used to quantify uncertainty in visual question answering models. |
| Outcome: | The proposed calibration score is better calibrated than in text-only models for in-context learning. |
Are VQA Systems RAD? Measuring Robustness to Augmented Data with Focused Interventions (2021.acl-short)
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| Challenge: | Existing systems for visual question answering are overfitted to training data and are sensitive to small perturbations. |
| Approach: | They propose a robustness measure to augment visual question answering datasets to measure generalization capabilities. |
| Outcome: | The proposed model can quantify failure cases which reveal that current systems are still brittle. |