Convolutional neural network (CNN)-based encoder-decoder models have profoundly
inspired recent works in the field of salient object detection (SOD). With the rapid …

Deep learning approaches have recently raised the bar in many fields, from Natural
Language Processing to Computer Vision, by leveraging large amounts of data. However …

In humans, Attention is a core property of all perceptual and cognitive operations. Given our
limited ability to process competing sources, attention mechanisms select, modulate, and …

With the rapid development of social economy, the traffic volume of urban roads has raised
significantly, which has led to increasingly serious urban traffic congestion problems, and …

In contrast to fully connected networks, Convolutional Neural Networks (CNNs) achieve
efficiency by learning weights associated with local filters with a finite spatial extent. An …

In this work we aim to predict the driver's focus of attention. The goal is to estimate what a
person would pay attention to while driving, and which part of the scene around the vehicle …

Visual saliency modeling for images and videos is treated as two independent tasks in
recent computer vision literature. While image saliency modeling is a well-studied problem …

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 …

Research in visual saliency has been focused on two major types of models namely fixation
prediction and salient object detection. The relationship between the two, however, has …

Audio information has been bypassed by most of current visual attention prediction studies.
However, sound could have influence on visual attention and such influence has been …