# Voila-A: Aligning Vision-Language Models with User's Gaze Attention

arXiv: https://arxiv.org/abs/2401.09454 · HTML: https://mlsanity.com/paper/2401.09454/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Placed in VLM benchmarks and visual reasoning, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 35 of its 62 references that are in the map.
- First posted on arXiv 22 Dec 2023.

## Placed in

- Multimodal LLMs / vision-language models / [VLM benchmarks and visual reasoning](https://mlsanity.com/mllm/vlm-benchmarks-and-visual-reasoning/)

## Builds on (most cited first, 30 of 35)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://mlsanity.com/paper/2304.10592/) (2023)
- [Kosmos-2: Grounding Multimodal Large Language Models to the World](https://mlsanity.com/paper/2306.14824/) (2023)
- [mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality](https://mlsanity.com/paper/2304.14178/) (2023)
- [Shikra: Unleashing Multimodal LLM's Referential Dialogue Magic](https://mlsanity.com/paper/2306.15195/) (2023)
- [LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention](https://mlsanity.com/paper/2303.16199/) (2023)
- [OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework](https://mlsanity.com/paper/2202.03052/) (2022)
- [RegionCLIP: Region-based Language-Image Pretraining](https://mlsanity.com/paper/2112.09106/) (2021)
- [OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models](https://mlsanity.com/paper/2308.01390/) (2023)
- [Otter: A Multi-Modal Model with In-Context Instruction Tuning](https://mlsanity.com/paper/2305.03726/) (2023)
- [Unifying Vision-and-Language Tasks via Text Generation](https://mlsanity.com/paper/2102.02779/) (2021)
- [Visual7W: Grounded Question Answering in Images](https://mlsanity.com/paper/1511.03416/) (2015)
- [GPT4RoI: Instruction Tuning Large Language Model on Region-of-Interest](https://mlsanity.com/paper/2307.03601/) (2023)
- [MultiModal-GPT: A Vision and Language Model for Dialogue with Humans](https://mlsanity.com/paper/2305.04790/) (2023)
- [Connecting Vision and Language with Localized Narratives](https://mlsanity.com/paper/1912.03098/) (2019)
- [Human Attention in Visual Question Answering: Do Humans and Deep Networks Look at the Same Regions?](https://mlsanity.com/paper/1606.03556/) (2016)
- [DeepGaze II: Reading fixations from deep features trained on object recognition](https://mlsanity.com/paper/1610.01563/) (2016)
- [Shallow and Deep Convolutional Networks for Saliency Prediction](https://mlsanity.com/paper/1603.00845/) (2016)
- [Object Referring in Videos with Language and Human Gaze](https://mlsanity.com/paper/1801.01582/) (2018)
- [Point and Ask: Incorporating Pointing into Visual Question Answering](https://mlsanity.com/paper/2011.13681/) (2020)
- [PEVL: Position-enhanced Pre-training and Prompt Tuning for Vision-language Models](https://mlsanity.com/paper/2205.11169/) (2022)
- [RegionBLIP: A Unified Multi-modal Pre-training Framework for Holistic and Regional Comprehension](https://mlsanity.com/paper/2308.02299/) (2023)
- [Connecting Vision and Language with Video Localized Narratives](https://mlsanity.com/paper/2302.11217/) (2023)
- [Seeing with Humans: Gaze-Assisted Neural Image Captioning](https://mlsanity.com/paper/1608.05203/) (2016)

## Built on by (most cited first, 28 of 28)

- [Gaze-VLM:Bridging Gaze and VLMs through Attention Regularization for Egocentric Understanding](https://mlsanity.com/paper/2510.21356/) (2025)
- [Where, What, Why: Towards Explainable Driver Attention Prediction](https://mlsanity.com/paper/2506.23088/) (2025)
- [Trends, Applications, and Challenges in Human Attention Modelling](https://mlsanity.com/paper/2402.18673/) (2024)
- [GazeVLM: A Vision-Language Model for Multi-Task Gaze Understanding](https://mlsanity.com/paper/2511.06348/) (2025)
- [Multi-speaker Attention Alignment for Multimodal Social Interaction](https://mlsanity.com/paper/2511.17952/) (2025)
- [Gaze-Regularized Vision-Language-Action Models for Robotic Manipulation](https://mlsanity.com/paper/2603.23202/) (2026)
- [G-VOILA: Gaze-Facilitated Information Querying in Daily Scenarios](https://mlsanity.com/paper/2405.07652/) (2024)
- [Connector-S: A Survey of Connectors in Multi-modal Large Language Models](https://mlsanity.com/paper/2502.11453/) (2025)
- [DWARF: Disease-weighted network for attention map refinement](https://mlsanity.com/paper/2406.17032/) (2024)
- [Gaze Heads: How VLMs Look at What They Describe](https://mlsanity.com/paper/2606.14703/) (2026)
- [GA3CE: Unconstrained 3D Gaze Estimation with Gaze-Aware 3D Context Encoding](https://mlsanity.com/paper/2505.10671/) (2025)
- [Eye Gaze Tells You Where to Compute: Gaze-Driven Efficient VLMs](https://mlsanity.com/paper/2509.16476/) (2025)
- [LENS: LLM-Enabled Narrative Synthesis for Mental Health by Aligning Multimodal Sensing with Language Models](https://mlsanity.com/paper/2512.23025/) (2025)
- [Integrating Cognitive Processing Signals into Language Models: A Review of Advances, Applications and Future Directions](https://mlsanity.com/paper/2504.06843/) (2025)
- [ChartGaze: Enhancing Chart Understanding in LVLMs with Eye-Tracking Guided Attention Refinement](https://mlsanity.com/paper/2509.13282/) (2025)
- [Deformable Attentive Visual Enhancement for Referring Segmentation Using Vision-Language Model](https://mlsanity.com/paper/2505.19242/) (2025)
- [Semantic visually-guided acoustic highlighting with large vision-language models](https://mlsanity.com/paper/2601.08871/) (2026)
- [Do You See What I Am Pointing At? Gesture-Based Egocentric Video Question Answering](https://mlsanity.com/paper/2603.12533/) (2026)
- [Resolving Ambiguity in Gaze-Facilitated Visual Assistant Interaction Paradigm](https://mlsanity.com/paper/2509.21980/) (2025)
- [Cross-attention Secretly Performs Orthogonal Alignment in Recommendation Models](https://mlsanity.com/paper/2510.09435/) (2025)
- [GazeQwen: Lightweight Gaze-Conditioned LLM Modulation for Streaming Video Understanding](https://mlsanity.com/paper/2603.25841/) (2026)
- [Through Their Eyes: Fixation-aligned Tuning for Personalized User Emulation](https://mlsanity.com/paper/2604.09368/) (2026)
- [PicoEyes: Unified Gaze Estimation Framework for Mixed Reality with a Large-Scale Multi-View Dataset](https://mlsanity.com/paper/2605.07188/) (2026)
- [EyeCue: Driver Cognitive Distraction Detection via Gaze-Empowered Egocentric Video Understanding](https://mlsanity.com/paper/2605.07859/) (2026)
- [Learning to See What You Need: Gaze Attention for Multimodal Large Language Models](https://mlsanity.com/paper/2605.13080/) (2026)
- [Your Mouse and Eyes Secretly Leak Your Preference: LLM Alignment using Implicit Feedback from Users](https://mlsanity.com/paper/2606.20482/) (2026)
- [Attention-Steered Vision-Language Models for Sign Language Translation](https://mlsanity.com/paper/2608.00235/) (2026)
- [How Much Does It Cost to Answer My Question? Benchmarking Cloud VLM-based VQA Systems](https://mlsanity.com/paper/2608.07861/) (2026)

## Abstract (arXiv)

In recent years, the integration of vision and language understanding has led to significant advancements in artificial intelligence, particularly through Vision-Language Models (VLMs). However, existing VLMs face challenges in handling real-world applications with complex scenes and multiple objects, as well as aligning their focus with the diverse attention patterns of human users. In this paper, we introduce gaze information, feasibly collected by AR or VR devices, as a proxy for human attention to guide VLMs and propose a novel approach, Voila-A, for gaze alignment to enhance the interpretability and effectiveness of these models in real-world applications. First, we collect hundreds of minutes of gaze data to demonstrate that we can mimic human gaze modalities using localized narratives. We then design an automatic data annotation pipeline utilizing GPT-4 to generate the VOILA-COCO dataset. Additionally, we innovate the Voila Perceiver modules to integrate gaze information into VLMs while preserving their pretrained knowledge. We evaluate Voila-A using a hold-out validation set and a newly collected VOILA-GAZE Testset, which features real-life scenarios captured with a gaze-tracking device. Our experimental results demonstrate that Voila-A significantly outperforms several baseline models. By aligning model attention with human gaze patterns, Voila-A paves the way for more intuitive, user-centric VLMs and fosters engaging human-AI interaction across a wide range of applications.