Due to object detection's close relationship with video analysis and image understanding, it
has attracted much research attention in recent years. Traditional object detection methods …

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 …

Saliency detection is one of the basic challenges in computer vision. Recently, CNNs are the
most widely used and powerful techniques for saliency detection, in which feature maps …

The detection of small infrared targets with low signal-to-noise ratios (SNRs) and contrasts in
noisy and cluttered backgrounds is challenging and therefore a domain of active research …

Salient object detection for RGB-D images aims to utilize color and depth information to
automatically localize objects of human interest in the scene and reduce the complexity of …

We present a learning model that makes full use of boundary information for salient object
segmentation. Specifically, we come up with a novel loss function, ie, Contour Loss, which …

Salient object detection models mimic the behavior of human beings and capture the most
salient region/object from the images or scenes, this field contains many important …

Recently, deep learning techniques have substantially boosted the performance of salient
object detection in still images. However, the salient object detection in videos by using …

Salient object detection (SOD) is a crucial task in the field of remote sensing image (RSI)
processing. Weakly supervised SOD methods, which generate saliency maps by …

As the conventional activation functions such as ReLU, LeakyReLU, and PReLU, the
negative parts in feature maps are simply truncated or linearized, which may result in …