GASP: Gated Attention For Saliency Prediction

TL;DR

This paper introduces GASP, a neural model that integrates social cues like gaze and affect to improve saliency prediction, demonstrating that social information enhances the accuracy of attention modeling.

Contribution

The paper presents a novel two-stage neural model that incorporates social cues into saliency prediction, with new fusion techniques and sub-networks for dynamic attention guidance.

Findings

Gaze and affective cues improve saliency prediction accuracy by at least 5%.

Fusion methods outperform non-fusion approaches for static integration.

Affective representations significantly enhance the model's performance.

Abstract

Saliency prediction refers to the computational task of modeling overt attention. Social cues greatly influence our attention, consequently altering our eye movements and behavior. To emphasize the efficacy of such features, we present a neural model for integrating social cues and weighting their influences. Our model consists of two stages. During the first stage, we detect two social cues by following gaze, estimating gaze direction, and recognizing affect. These features are then transformed into spatiotemporal maps through image processing operations. The transformed representations are propagated to the second stage (GASP) where we explore various techniques of late fusion for integrating social cues and introduce two sub-networks for directing attention to relevant stimuli. Our experiments indicate that fusion approaches achieve better results for static integration methods, whereas non-fusion approaches for which the influence of each modality is unknown, result in better outcomes when coupled with recurrent models for dynamic saliency prediction. We show that gaze direction and affective representations contribute a prediction to ground-truth correspondence improvement of at least 5% compared to dynamic saliency models without social cues. Furthermore, affective representations improve GASP, supporting the necessity of considering affect-biased attention in predicting saliency.