Unified Dynamic Scanpath Predictors Outperform Individually Trained Neural Models

TL;DR

This paper introduces a deep learning model that predicts human scanpaths in videos by integrating social cues and fixation history, outperforming individual models and enabling a unified approach for diverse attentional behaviors.

Contribution

The study presents a novel social cue integration model for scanpath prediction that trains a single unified model, capturing individual differences and universal attention more effectively.

Findings

Unified model performs on par or better than individual models.

Late neural integration surpasses early fusion on large datasets.

Fixation history enables training a single model for diverse scanpaths.

Abstract

Previous research on scanpath prediction has mainly focused on group models, disregarding the fact that the scanpaths and attentional behaviors of individuals are diverse. The disregard of these differences is especially detrimental to social human-robot interaction, whereby robots commonly emulate human gaze based on heuristics or predefined patterns. However, human gaze patterns are heterogeneous and varying behaviors can significantly affect the outcomes of such human-robot interactions. To fill this gap, we developed a deep learning-based social cue integration model for saliency prediction to instead predict scanpaths in videos. Our model learned scanpaths by recursively integrating fixation history and social cues through a gating mechanism and sequential attention. We evaluated our approach on gaze datasets of dynamic social scenes, observed under the free-viewing condition. The introduction of fixation history into our models makes it possible to train a single unified model rather than the resource-intensive approach of training individual models for each set of scanpaths. We observed that the late neural integration approach surpasses early fusion when training models on a large dataset, in comparison to a smaller dataset with a similar distribution. Results also indicate that a single unified model, trained on all the observers' scanpaths, performs on par or better than individually trained models. We hypothesize that this outcome is a result of the group saliency representations instilling universal attention in the model, while the supervisory signal and fixation history guide it to learn personalized attentional behaviors, providing the unified model a benefit over individual models due to its implicit representation of universal attention.