Visual attention is one of the most significant characteristics for selecting and understanding
the outside redundancy world. The human vision system cannot process all information …

Deep saliency models can effectively imitate the attention mechanism of human vision, and
they perform considerably better than classical models that rely on handcrafted features …

Deep neural networks have shown their profound impact on achieving human-level
performance in visual saliency prediction. However, it is still unclear how they learn their …

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

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

In this paper, we proposed an integrated model of semantic-aware and contrast-aware
saliency combining both bottom-up and top-down cues for effective saliency estimation and …

Saliency computation models aim to imitate the attention mechanism in the human visual
system. The application of deep neural networks for saliency prediction has led to a drastic …

Computational saliency models for still images have gained significant popularity in recent
years. Saliency prediction from videos, on the other hand, has received relatively little …

Predicting human visual attention cannot only increase our understanding of the underlying
biological mechanisms, but also bring new insights for other computer vision-related tasks …

Predicting salient regions in natural images requires the detection of objects that are present
in a scene. To develop robust representations for this challenging task, high-level visual …