# GazeXplain: Learning to Predict Natural Language Explanations of Visual Scanpaths

arXiv: https://arxiv.org/abs/2408.02788 · HTML: https://mlsanity.com/paper/2408.02788/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 54 of its 102 references that are in the map.
- First posted on arXiv 5 Aug 2024.

## Builds on (most cited first, 30 of 54)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [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)
- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [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)
- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [Microsoft COCO Captions: Data Collection and Evaluation Server](https://mlsanity.com/paper/1504.00325/) (2015)
- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (2015)
- [Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering](https://mlsanity.com/paper/1707.07998/) (2017)
- [Show and Tell: A Neural Image Caption Generator](https://mlsanity.com/paper/1411.4555/) (2014)
- [Self-critical Sequence Training for Image Captioning](https://mlsanity.com/paper/1612.00563/) (2016)
- [Attention on Attention for Image Captioning](https://mlsanity.com/paper/1908.06954/) (2019)
- [What does CLIP know about a red circle? Visual prompt engineering for VLMs](https://mlsanity.com/paper/2304.06712/) (2023)
- [What do different evaluation metrics tell us about saliency models?](https://mlsanity.com/paper/1604.03605/) (2016)
- [Multimodal Explanations: Justifying Decisions and Pointing to the Evidence](https://mlsanity.com/paper/1802.08129/) (2018)
- [Visual Storytelling](https://mlsanity.com/paper/1604.03968/) (2016)
- [Generating Visual Explanations](https://mlsanity.com/paper/1603.08507/) (2016)
- [Predicting Human Eye Fixations via an LSTM-based Saliency Attentive Model](https://mlsanity.com/paper/1611.09571/) (2016)
- [Unsupervised Image Captioning](https://mlsanity.com/paper/1811.10787/) (2018)
- [Faithful Multimodal Explanation for Visual Question Answering](https://mlsanity.com/paper/1809.02805/) (2018)
- [Self-Critical Reasoning for Robust Visual Question Answering](https://mlsanity.com/paper/1905.09998/) (2019)

## Built on by (most cited first, 15 of 15)

- [Where, What, Why: Towards Explainable Driver Attention Prediction](https://mlsanity.com/paper/2506.23088/) (2025)
- [Modeling Human Gaze Behavior with Diffusion Models for Unified Scanpath Prediction](https://mlsanity.com/paper/2507.23021/) (2025)
- [Reading Recognition in the Wild](https://mlsanity.com/paper/2505.24848/) (2025)
- [FSDAM: Few-Shot Driving Attention Modeling via Vision-Language Coupling](https://mlsanity.com/paper/2511.12708/) (2025)
- [From Scene to Object: Text-Guided Dual-Gaze Prediction](https://mlsanity.com/paper/2604.20191/) (2026)
- [DeepGaze3.5-VL: Modeling Scanpaths via Autoregressive Token Prediction](https://mlsanity.com/paper/2607.02083/) (2026)
- [SIGN: A Statistically-Informed Gaze Network for Gaze Time Prediction](https://mlsanity.com/paper/2501.17422/) (2025)
- [Decoding Open-Ended Information Seeking Goals from Eye Movements in Reading](https://mlsanity.com/paper/2505.02872/) (2025)
- [EVA: Bridging Performance and Human Alignment in Hard-Attention Vision Models for Image Classification](https://mlsanity.com/paper/2603.27340/) (2026)
- [ViASNet: A Video Ad Saliency Network for Predicting Dynamic Saliency and Viewer Engagement](https://mlsanity.com/paper/2605.29302/) (2026)
- [ST-DiffEye: Diffusion-based Continuous Gaze Generation via Joint Scanpath-Trajectory Modeling](https://mlsanity.com/paper/2606.15486/) (2026)
- [Gaze-to-text Generation: Beyond Categorical Decoding of Human Attention](https://mlsanity.com/paper/2607.23917/) (2026)
- [Focus Where It Counts: A Salience-Driven Vision-Language Model for Low Vision Assistance](https://mlsanity.com/paper/2608.28218/) (2026)
- [OpenVAM: Open-World Visual Attention Modeling with VLMs](https://mlsanity.com/paper/2609.31364/) (2026)
- [FocusDrive: Reasoning with Visual Focus for Autonomous Driving](https://mlsanity.com/paper/2609.33190/) (2026)

## Abstract (arXiv)

While exploring visual scenes, humans' scanpaths are driven by their underlying attention processes. Understanding visual scanpaths is essential for various applications. Traditional scanpath models predict the where and when of gaze shifts without providing explanations, creating a gap in understanding the rationale behind fixations. To bridge this gap, we introduce GazeXplain, a novel study of visual scanpath prediction and explanation. This involves annotating natural-language explanations for fixations across eye-tracking datasets and proposing a general model with an attention-language decoder that jointly predicts scanpaths and generates explanations. It integrates a unique semantic alignment mechanism to enhance the consistency between fixations and explanations, alongside a cross-dataset co-training approach for generalization. These novelties present a comprehensive and adaptable solution for explainable human visual scanpath prediction. Extensive experiments on diverse eye-tracking datasets demonstrate the effectiveness of GazeXplain in both scanpath prediction and explanation, offering valuable insights into human visual attention and cognitive processes.