An Inter-Observer Consistent Deep Adversarial Training for Visual Scanpath Prediction

The visual scanpath represents the fundamental concept upon which visual attention research is based. As a result, the ability to predict them has emerged as a crucial task in recent years. It is represented as a sequence of points through which the human gaze moves while exploring a scene. In this paper, we propose an inter-observer consistent adversarial training approach for scanpath prediction through a lightweight deep neural network. The proposed method employs a discriminative neural network as a dynamic loss that better models the natural stochastic phenomenon while maintaining consistency between the distributions related to the subjective nature of scanpaths traversed by different observers. The competitiveness of our approach against state-of-the-art methods is shown through a testing phase.