Abstract Transformers are Deep Neural Networks (DNN) that utilize a self-attention
mechanism to capture contextual relationships within sequential data. Unlike traditional …

A long time ago in the machine learning literature, the idea of incorporating a mechanism
inspired by the human visual system into neural networks was introduced. This idea is …

Semantic segmentation of remote sensing images plays an important role in a wide range of
applications, including land resource management, biosphere monitoring, and urban …

In this paper, we present a novel end-to-end learning neural network, ie, MATNet, for zero-
shot video object segmentation (ZVOS). Motivated by the human visual attention behavior …

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 …

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 …

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 …

This paper conducts a systematic study on the role of visual attention in Unsupervised Video
Object Segmentation (UVOS) tasks. By elaborately annotating three popular video …

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 …

Modeling visual attention-particularly stimulus-driven, saliency-based attention-has been a
very active research area over the past 25 years. Many different models of attention are now …