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This paper introduces a new framework to predict visual attention ofomnidirectional images. The key setup of our architecture is the simultaneousprediction of the saliency map and a corresponding scanpath for a givenstimulus. The framework implements a fully encoder-decoder convolutional neuralnetwork augmented by an attention module to generate representative saliencymaps. In addition, an auxiliary network is employed to generate probableviewport center fixation points through the SoftArgMax function. The latterallows to derive fixation points from feature maps. To take advantage of thescanpath prediction, an adaptive joint probability distribution model is thenapplied to construct the final unbiased saliency map by leveraging the encoderdecoder-based saliency map and the scanpath-based saliency heatmap. Theproposed framework was evaluated in terms of saliency and scanpath prediction,and the results were compared to state-of-the-art methods on Salient360!dataset. The results showed the relevance of our framework and the benefits ofsuch architecture for further omnidirectional visual attention predictiontasks.

SalyPath360: Saliency and Scanpath Prediction Framework for Omnidirectional Images