Current RGB-D salient object detection (RGB-D SOD) methods mainly develop a
generalizable model trained by binary cross-entropy (BCE) loss based on convolutional or …

Estimating a scene's depth to achieve collision avoidance against moving pedestrians is a
crucial and fundamental problem in the robotic field. This paper proposes a novel, low …

Fault samples obtained in real-world environment are limited, which makes it hard to
diagnose faults of rotating machines (RM) accurately by using the existing intelligent …

Self-supervised depth estimation has evolved into an image reconstruction task that
minimizes a photometric loss. While recent methods have made strides in indoor depth …

Zero-faulty sample machinery fault detection is extremely challenging. However, it is
common issue that fault samples are extremely difficult to obtain, which limits the …

Stereophotogrammetry is an emerging technique of scene understanding. Its origins go
back to at least the 1800s when people first started to investigate using photographs to …

Current computer vision tasks based on deep learning require a huge amount of data with
annotations for model training or testing, especially in some dense estimation tasks, such as …

Knowing the distance to nearby objects is crucial for autonomous cars to navigate safely in
everyday traffic. In this paper, we investigate monocular depth estimation, which advanced …

Current computer vision tasks based on deep learning require a huge amount of data with
annotations for model training or testing, especially in some dense estimation tasks, such as …