Estimation of the human pose from a monocular camera has been an emerging research
topic in the computer vision community with many applications. Recently, benefiting from the …

Face recognition (FR) is one of the most popular and long-standing topics in computer
vision. With the recent development of deep learning techniques and large-scale datasets …

In this paper, we design a simple yet powerful deep network architecture, U 2-Net, for salient
object detection (SOD). The architecture of our U 2-Net is a two-level nested U-structure. The …

High-resolution representations are essential for position-sensitive vision problems, such as
human pose estimation, semantic segmentation, and object detection. Existing state-of-the …

In this paper, we are interested in the human pose estimation problem with a focus on
learning reliable high-resolution representations. Most existing methods recover high …

High-resolution representation learning plays an essential role in many vision problems, eg,
pose estimation and semantic segmentation. The high-resolution network (HRNet)~\cite …

Vision-based monocular human pose estimation, as one of the most fundamental and
challenging problems in computer vision, aims to obtain posture of the human body from …

In this paper, we study the problem of learning Graph Convolutional Networks (GCNs) for
regression. Current architectures of GCNs are limited to the small receptive field of …

Heatmap regression with a deep network has become one of the mainstream approaches to
localize facial landmarks. However, the loss function for heatmap regression is rarely …

Existing pose estimation approaches fall into two categories: single-stage and multi-stage
methods. While multi-stage methods are seemingly more suited for the task, their …