The aim of multi-output learning is to simultaneously predict multiple outputs given an input.
It is an important learning problem for decision-making since making decisions in the real …

In this paper, we aim to predict human eye fixation with view-free scenes based on an end-to-
end deep learning architecture. Although convolutional neural networks (CNNs) have made …

The human attention mechanism can be understood and simulated by closely associating
the saliency prediction task to neuroscience and psychology. Furthermore, saliency …

This paper presents a novel spherical convolutional neural network based scheme for
saliency detection for 360 videos. Specifically, in our spherical convolution neural network …

Scanpath prediction for 360deg images aims to produce dynamic gaze behaviors based on
the human visual perception mechanism. Most existing scanpath prediction methods for …

Convolutional neural networks (CNNs) have significantly advanced computational
modelling for saliency prediction. However, accurately simulating the mechanisms of visual …

Spherical images or videos, as typical non-euclidean data, are usually stored in the form of
2D panoramas obtained through an equirectangular projection, which is neither equal area …

Feed-forward only convolutional neural networks (CNNs) may ignore intrinsic relationships
and potential benefits of feedback connections in vision tasks such as saliency detection …

Eye movement is a new type of data for cartography and geographic information science
(GIS) research. However, previous studies rarely built eye movement datasets with …

Visual attention is a field with a considerable history, with eye movement control and
prediction forming an important subfield. Fixation modeling in the past decades has been …