FEW-SHOT PERSONALIZED SALIENCY PREDICTION WITH SIMILARITY OF GAZE TENDENCY USING OBJECT-BASED STRUCTURAL INFORMATION

This paper presents a few-shot personalized saliency prediction method with similarity of gaze tendency using object-based structural information. The personalized saliency maps (PSMs) that represent the individual visual attention can be used for analyzing the heterogeneity among personalized preferences, whereas general saliency maps ignore the individual differences. However, the PSM prediction is a difficult task since the acquisition of eye tracking data, which are needed for obtaining PSMs, gives persons a heavy burden. Then, for realizing PSM prediction with a limited amount of training data, the use of the similarity of gaze tendency between persons can be one effective way. It has been reported that human gazes are related to objects and their relative relationships that are semantic and structural information, and we focus on the integration of PSMs predicted for other persons by using object-based similarities of gaze tendency. The advantage in this paper is that we newly focus on similarities of gaze tendency for visually similar objects for solving the lack of eye tracking data by considering both semantic and structural information, simultaneously. By experimenting with the open dataset, the proposed method outperforms the state-of-the-art methods.