Semantic segmentation is a challenging task in computer vision. In recent years, the
performance of semantic segmentation has been greatly improved by using deep learning …

Object detection is a fundamental visual recognition problem in computer vision and has
been widely studied in the past decades. Visual object detection aims to find objects of …

Bounding box regression (BBR) has been widely used in object detection and instance
segmentation, which is an important step in object localization. However, most of the existing …

Deep learning-based object detection and instance segmentation have achieved
unprecedented progress. In this article, we propose complete-IoU (CIoU) loss and Cluster …

Understanding the world in 3D is a critical component of urban autonomous driving.
Generally, the combination of expensive LiDAR sensors and stereo RGB imaging has been …

Pedestrian detection in crowded scenes is a challenging problem since the pedestrians
often gather together and occlude each other. In this paper, we propose a new occlusion …

Multispectral pedestrian detection is capable of adapting to insufficient illumination
conditions by leveraging color-thermal modalities. On the other hand, it is still lacking of in …

One fundamental problem in Earth Vision is to accurately find the locations and identify the
categories of the interesting objects in the aerial images, for which oriented bounding boxes …

Multispectral images of color-thermal pairs have shown more effective than a single color
channel for pedestrian detection, especially under challenging illumination conditions …

Gait, the walking pattern of individuals, is one of the most important biometrics modalities.
Most of the existing gait recognition methods take silhouettes or articulated body models as …