A domain adaptive deep learning solution for scanpath prediction of paintings

TL;DR

This paper introduces a novel deep learning model that predicts eye-movement scanpaths on paintings by leveraging domain adaptation techniques, outperforming existing methods in accuracy and efficiency.

Contribution

It presents a new architecture combining FCNN, differentiable modules, and domain adaptation to predict human visual attention on paintings, addressing cross-domain shifts.

Findings

Outperforms state-of-the-art in accuracy

Achieves higher efficiency in scanpath prediction

Effectively handles domain shifts between natural images and paintings

Abstract

Cultural heritage understanding and preservation is an important issue for society as it represents a fundamental aspect of its identity. Paintings represent a significant part of cultural heritage, and are the subject of study continuously. However, the way viewers perceive paintings is strictly related to the so-called HVS (Human Vision System) behaviour. This paper focuses on the eye-movement analysis of viewers during the visual experience of a certain number of paintings. In further details, we introduce a new approach to predicting human visual attention, which impacts several cognitive functions for humans, including the fundamental understanding of a scene, and then extend it to painting images. The proposed new architecture ingests images and returns scanpaths, a sequence of points featuring a high likelihood of catching viewers' attention. We use an FCNN (Fully Convolutional Neural Network), in which we exploit a differentiable channel-wise selection and Soft-Argmax modules. We also incorporate learnable Gaussian distributions onto the network bottleneck to simulate visual attention process bias in natural scene images. Furthermore, to reduce the effect of shifts between different domains (i.e. natural images, painting), we urge the model to learn unsupervised general features from other domains using a gradient reversal classifier. The results obtained by our model outperform existing state-of-the-art ones in terms of accuracy and efficiency.