# On the Role of Visual Grounding in VQA

arXiv: https://arxiv.org/abs/2406.18253 · HTML: https://mlsanity.com/paper/2406.18253/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 41 of its 46 references that are in the map.
- First posted on arXiv 26 Jun 2024.

## Builds on (most cited first, 30 of 41)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [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)
- [Shortcut Learning in Deep Neural Networks](https://mlsanity.com/paper/2004.07780/) (2020)
- [LXMERT: Learning Cross-Modality Encoder Representations from Transformers](https://mlsanity.com/paper/1908.07490/) (2019)
- [CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning](https://mlsanity.com/paper/1612.06890/) (2016)
- [Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering](https://mlsanity.com/paper/1707.07998/) (2017)
- [Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness?](https://mlsanity.com/paper/2004.03685/) (2020)
- [ERASER: A Benchmark to Evaluate Rationalized NLP Models](https://mlsanity.com/paper/1911.03429/) (2019)
- [Don't Just Assume; Look and Answer: Overcoming Priors for Visual Question Answering](https://mlsanity.com/paper/1712.00377/) (2017)
- [Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations](https://mlsanity.com/paper/1703.03717/) (2017)
- [Understanding Neural Networks through Representation Erasure](https://mlsanity.com/paper/1612.08220/) (2016)
- [Compositional Attention Networks for Machine Reasoning](https://mlsanity.com/paper/1803.03067/) (2018)
- [Taking a HINT: Leveraging Explanations to Make Vision and Language Models More Grounded](https://mlsanity.com/paper/1902.03751/) (2019)
- [Ground Truth Evaluation of Neural Network Explanations with CLEVR-XAI](https://mlsanity.com/paper/2003.07258/) (2020)
- [Analyzing the Behavior of Visual Question Answering Models](https://mlsanity.com/paper/1606.07356/) (2016)
- [Learning by Abstraction: The Neural State Machine](https://mlsanity.com/paper/1907.03950/) (2019)
- [Human Attention in Visual Question Answering: Do Humans and Deep Networks Look at the Same Regions?](https://mlsanity.com/paper/1606.03556/) (2016)
- [Yin and Yang: Balancing and Answering Binary Visual Questions](https://mlsanity.com/paper/1511.05099/) (2015)
- [On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law](https://mlsanity.com/paper/2005.09241/) (2020)
- [Beyond Question-Based Biases: Assessing Multimodal Shortcut Learning in Visual Question Answering](https://mlsanity.com/paper/2104.03149/) (2021)
- [Roses Are Red, Violets Are Blue... but Should Vqa Expect Them To?](https://mlsanity.com/paper/2006.05121/) (2020)
- [MUTANT: A Training Paradigm for Out-of-Distribution Generalization in Visual Question Answering](https://mlsanity.com/paper/2009.08566/) (2020)
- [Self-Critical Reasoning for Robust Visual Question Answering](https://mlsanity.com/paper/1905.09998/) (2019)
- [Grounding Answers for Visual Questions Asked by Visually Impaired People](https://mlsanity.com/paper/2202.01993/) (2022)
- [Explicit Bias Discovery in Visual Question Answering Models](https://mlsanity.com/paper/1811.07789/) (2018)

## Built on by (most cited first, 5 of 5)

- [A Study on Neuro-Symbolic Artificial Intelligence: Healthcare Perspectives](https://mlsanity.com/paper/2503.18213/) (2025)
- [Mitigating Easy Option Bias in Multiple-Choice Question Answering](https://mlsanity.com/paper/2508.13428/) (2025)
- [IRIS: Intent Resolution via Inference-time Saccades for Open-Ended VQA in Large Vision-Language Models](https://mlsanity.com/paper/2602.16138/) (2026)
- [Seeing Before Agreeing: Aligning Multi-Agent Consensus with Visual Evidence](https://mlsanity.com/paper/2605.30698/) (2026)
- [The ART of Composition: Attention-Regularized Training for Compositional Visual Grounding](https://mlsanity.com/paper/2412.08110/) (2024)

## Abstract (arXiv)

Visual Grounding (VG) in VQA refers to a model's proclivity to infer answers based on question-relevant image regions. Conceptually, VG identifies as an axiomatic requirement of the VQA task. In practice, however, DNN-based VQA models are notorious for bypassing VG by way of shortcut (SC) learning without suffering obvious performance losses in standard benchmarks. To uncover the impact of SC learning, Out-of-Distribution (OOD) tests have been proposed that expose a lack of VG with low accuracy. These tests have since been at the center of VG research and served as basis for various investigations into VG's impact on accuracy. However, the role of VG in VQA still remains not fully understood and has not yet been properly formalized. In this work, we seek to clarify VG's role in VQA by formalizing it on a conceptual level. We propose a novel theoretical framework called "Visually Grounded Reasoning" (VGR) that uses the concepts of VG and Reasoning to describe VQA inference in ideal OOD testing. By consolidating fundamental insights into VG's role in VQA, VGR helps to reveal rampant VG-related SC exploitation in OOD testing, which explains why the relationship between VG and OOD accuracy has been difficult to define. Finally, we propose an approach to create OOD tests that properly emphasize a requirement for VG, and show how to improve performance on them.