Human Action Recognition (HAR) aims to understand human behavior and assign a label to
each action. It has a wide range of applications, and therefore has been attracting increasing …

Depth estimation is a classic task in computer vision, which is of great significance for many
applications such as augmented reality, target tracking and autonomous driving. Traditional …

Estimating the accurate depth from a single image is challenging since it is inherently
ambiguous and ill-posed. While recent works design increasingly complicated and powerful …

The success of monocular depth estimation relies on large and diverse training sets. Due to
the challenges associated with acquiring dense ground-truth depth across different …

In this paper, we propose a robust and efficient end-to-end non-local spatial propagation
network for depth completion. The proposed network takes RGB and sparse depth images …

In this paper, we investigate the issue of knowledge distillation for training compact semantic
segmentation networks by making use of cumbersome networks. We start from the …

While convolutional neural networks have shown a tremendous impact on various computer
vision tasks, they generally demonstrate limitations in explicitly modeling long-range …

Self-supervised learning has shown very promising results for monocular depth estimation.
Scene structure and local details both are significant clues for high-quality depth estimation …

Single-view depth prediction is a fundamental problem in computer vision. Recently, deep
learning methods have led to significant progress, but such methods are limited by the …

Computer vision technology is being used by many but remains representative of only a few.
People have reported misbehavior of computer vision models, including offensive prediction …