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 …

Prohibited items detection in X-ray images often plays an important role in protecting public
safety, which often deals with color-monotonous and luster-insufficient objects, resulting in …

Although refinement is commonly used in visual tasks to improve pre-obtained results, it has
not been studied for Multiple Object Tracking (MOT) tasks. This could be attributed to two …

Local feature matching constitutes the cornerstone of multiple computer vision applications
(eg, 3D reconstruction and long-term visual localization), and has been successfully …

This paper addresses the core issue of how to learn powerful features for saliency. We have
two major observations. First, feature maps of different layers in convolutional neural …

In recent years, the detection of distinctive objects in stereoscopic 3D images has drawn
increasing attention. Unlike 2D salient object detection, salient object detection in …

Deep learning techniques have largely solved the problem of rail surface defect detection
(SDD), however, two aspects have yet to be addressed. In most existing approaches, two …

Super-resolution (SR) algorithms have now become a bottleneck for several remote sensing
applications. SR is a technique that enhances minute details of the image by increasing …

In this paper, we identify and address a serious design bias of existing salient object
detection (SOD) datasets, which unrealistically assume that each image should contain at …

In this paper, we present a method called density-aware convolutional neural network
(DensityCNN) to perform the crowd counting task in various crowded scenes. The key idea …