Few-shot Personalized Scanpath Prediction

2025-04-07

AI Summary

To address the challenge of personalized scanpath prediction under few-shot settings, this paper introduces the novel Few-Shot Personalized Scanpath Prediction (FS-PSP) task. To overcome limitations of conventional models—namely, heavy reliance on large-scale annotated data and poor adaptability to new users—we propose the Subject-Embedding Network (SE-Net), which jointly models cross-subject discriminability and intra-subject consistency for efficient individual representation learning. Furthermore, we design a conditional scanpath generation model and adopt a fine-tuning-free inference paradigm. Evaluated on multiple eye-tracking datasets, our method achieves high-accuracy personalized predictions using only 1–5 support scanpaths per subject, significantly outperforming state-of-the-art approaches. Experimental results demonstrate strong generalization across unseen users and practical applicability in real-world low-data scenarios.

Technology Category

• Search and Optimization: Learning to Search
• Computer Vision: Motion & Tracking
• Machine Learning: Privacy

Application Category

• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• Security and Privacy: Large-scale security measurements

Abstract

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

Problem

• Predict scanpaths for unseen subjects with minimal data
• Address data-intensive training in personalized scanpath prediction
• Capture unique individual scanpath patterns efficiently

Innovation

• Uses Subject-Embedding Network (SE-Net)
• Generates personalized subject embeddings
• Requires minimal support data