# Few-shot Personalized Scanpath Prediction

arXiv: https://arxiv.org/abs/2504.05499 · HTML: https://mlsanity.com/paper/2504.05499/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 64 references that are in the map.
- First posted on arXiv 7 Apr 2025.

## Builds on (most cited first, 29 of 29)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment](https://mlsanity.com/paper/1908.06391/) (2019)
- [Few-Shot Learning via Embedding Adaptation with Set-to-Set Functions](https://mlsanity.com/paper/1812.03664/) (2018)
- [Adaptive Prototype Learning and Allocation for Few-Shot Segmentation](https://mlsanity.com/paper/2104.01893/) (2021)
- [Learnable Fourier Features for Multi-Dimensional Spatial Positional Encoding](https://mlsanity.com/paper/2106.02795/) (2021)
- [Few-Shot Adaptive Gaze Estimation](https://mlsanity.com/paper/1905.01941/) (2019)
- [Meta Faster R-CNN: Towards Accurate Few-Shot Object Detection with Attentive Feature Alignment](https://mlsanity.com/paper/2104.07719/) (2021)
- [Predicting Goal-directed Human Attention Using Inverse Reinforcement Learning](https://mlsanity.com/paper/2005.14310/) (2020)
- [Generating Representative Samples for Few-Shot Classification](https://mlsanity.com/paper/2205.02918/) (2022)
- [Gazeformer: Scalable, Effective and Fast Prediction of Goal-Directed Human Attention](https://mlsanity.com/paper/2303.15274/) (2023)
- [PathGAN: Visual Scanpath Prediction with Generative Adversarial Networks](https://mlsanity.com/paper/1809.00567/) (2018)
- [Few-Shot Classification with Contrastive Learning](https://mlsanity.com/paper/2209.08224/) (2022)
- [SaltiNet: Scan-path Prediction on 360 Degree Images using Saliency Volumes](https://mlsanity.com/paper/1707.03123/) (2017)
- [Variational Feature Disentangling for Fine-Grained Few-Shot Classification](https://mlsanity.com/paper/2010.03255/) (2020)
- [Unifying Top-down and Bottom-up Scanpath Prediction Using Transformers](https://mlsanity.com/paper/2303.09383/) (2023)
- [Generating Features with Increased Crop-related Diversity for Few-Shot Object Detection](https://mlsanity.com/paper/2304.05096/) (2023)
- [Target-absent Human Attention](https://mlsanity.com/paper/2207.01166/) (2022)
- [Contrastive Learning for Prompt-Based Few-Shot Language Learners](https://mlsanity.com/paper/2205.01308/) (2022)
- [Personalized Saliency and its Prediction](https://mlsanity.com/paper/1710.03011/) (2017)
- [Beyond Average: Individualized Visual Scanpath Prediction](https://mlsanity.com/paper/2404.12235/) (2024)
- [EyeFormer: Predicting Personalized Scanpaths with Transformer-Guided Reinforcement Learning](https://mlsanity.com/paper/2404.10163/) (2024)
- [Scanpath Prediction on Information Visualisations](https://mlsanity.com/paper/2112.02340/) (2021)
- [Look Hear: Gaze Prediction for Speech-directed Human Attention](https://mlsanity.com/paper/2407.19605/) (2024)
- [SalGaze: Personalizing Gaze Estimation Using Visual Saliency](https://mlsanity.com/paper/1910.10603/) (2019)
- [Learning User Embeddings from Human Gaze for Personalised Saliency Prediction](https://mlsanity.com/paper/2403.13653/) (2024)

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

- [Estimating cognitive biases with attention-aware inverse planning](https://mlsanity.com/paper/2510.25951/) (2025)
- [Personalized Image Descriptions from Attention Sequences](https://mlsanity.com/paper/2512.06662/) (2025)
- [DeepGaze3.5-VL: Modeling Scanpaths via Autoregressive Token Prediction](https://mlsanity.com/paper/2607.02083/) (2026)
- [Infinite Gaze Generation for Videos with Autoregressive Diffusion](https://mlsanity.com/paper/2603.24938/) (2026)
- [Importance-Aware OBS Pruning for Diffusion Models](https://mlsanity.com/paper/2607.20048/) (2026)

## Abstract (arXiv)

A personalized model for scanpath prediction provides insights into the visual preferences and attention patterns of individual subjects. However, existing methods for training scanpath prediction models are data-intensive and cannot be effectively personalized to new individuals with only a few available examples. In this paper, we propose few-shot personalized scanpath prediction task (FS-PSP) and a novel method to address it, which aims to predict scanpaths for an unseen subject using minimal support data of that subject's scanpath behavior. The key to our method's adaptability is the Subject-Embedding Network (SE-Net), specifically designed to capture unique, individualized representations for each subject's scanpaths. SE-Net generates subject embeddings that effectively distinguish between subjects while minimizing variability among scanpaths from the same individual. The personalized scanpath prediction model is then conditioned on these subject embeddings to produce accurate, personalized results. Experiments on multiple eye-tracking datasets demonstrate that our method excels in FS-PSP settings and does not require any fine-tuning steps at test time. Code is available at: https://github.com/cvlab-stonybrook/few-shot-scanpath