With the advent of state of the art nature-inspired pure attention based models ie
transformers, and their success in natural language processing (NLP), their extension to …

Content-based image analysis and computer vision techniques are used in various health-
care systems to detect the diseases. The abnormalities in a human eye are detected through …

As an essential problem in computer vision, salient object detection (SOD) has attracted an
increasing amount of research attention over the years. Recent advances in SOD are …

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 …

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 …

The performance of traditional visual inspection by handcrafted features for crack and
leakage defects of metro shield tunnel is hardly satisfactory nowadays because it is low …

Our work proposes a novel deep learning framework for estimating crowd density from static
images of highly dense crowds. We use a combination of deep and shallow, fully …

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 …

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 …

This paper explores gaze prediction in dynamic $360^ circ $ immersive videos, emph {ie},
based on the history scan path and VR contents, we predict where a viewer will look at an …