Few-shot Personalized Saliency Prediction Based on Interpersonal Gaze Patterns

TL;DR

This paper introduces a few-shot personalized saliency prediction method that uses interpersonal gaze patterns, image selection, and tensor-based regression to predict individual visual attention with limited data.

Contribution

It presents a novel approach combining image selection and structural preservation to improve few-shot personalized saliency prediction.

Findings

Image selection enhances gaze pattern diversity.

Tensor-based regression preserves structural information.

Proposed method outperforms baseline approaches.

Abstract

This study proposes a few-shot personalized saliency prediction method that leverages interpersonal gaze patterns. Unlike general saliency maps, personalized saliency maps (PSMs) capture individual visual attention and provide insights into individual visual preferences. However, predicting PSMs is challenging because of the complexity of gaze patterns and the difficulty of collecting extensive eye-tracking data from individuals. An effective strategy for predicting PSMs from limited data is the use of eye-tracking data from other persons. To efficiently handle the PSMs of other persons, this study focuses on the selection of images to acquire eye-tracking data and the preservation of the structural information of PSMs. In the proposed method, these images are selected such that they bring more diverse gaze patterns to persons, and structural information is preserved using tensor-based regression. The experimental results demonstrate that these two factors are beneficial for few-shot PSM prediction.