Detecting and segmenting salient objects from natural scenes, often referred to as salient
object detection, has attracted great interest in computer vision. While many models have …

As we navigate and behave in the world, we are constantly deciding, a few times per
second, where to look next. The outcomes of these decisions in response to visual input are …

This paper proposes a human-aware deblurring model that disentangles the motion blur
between foreground (FG) humans and background (BG). The proposed model is based on a …

Data-driven saliency has recently gained a lot of attention thanks to the use of convolutional
neural networks for predicting gaze fixations. In this paper, we go beyond standard …

Predicting where people look in static scenes, aka visual saliency, has received significant
research interest recently. However, relatively less effort has been spent in understanding …

Understanding how people explore immersive virtual environments is crucial for many
applications, such as designing virtual reality (VR) content, developing new compression …

We introduce SalGAN, a deep convolutional neural network for visual saliency prediction
trained with adversarial examples. The first stage of the network consists of a generator …

How best to evaluate a saliency model's ability to predict where humans look in images is an
open research question. The choice of evaluation metric depends on how saliency is …

In this work, we contribute to video saliency research in two ways. First, we introduce a new
benchmark for predicting human eye movements during dynamic scene free-viewing, which …

Visual saliency models have enjoyed a big leap in performance in recent years, thanks to
advances in deep learning and large scale annotated data. Despite enormous effort and …