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20172020
most citedMonocular Human Pose Estimation: A Survey of Deep Learning-based Methods

447 citations · 499 across the 9 of their papers we have counts for

collaborators

9 papers

cs.CV2020447 cited

Monocular Human Pose Estimation: A Survey of Deep Learning-based Methods

Yucheng Chen, Yingli Tian, Mingyi He

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 input images…

cs.CV202012 cited

Self-supervised Modal and View Invariant Feature Learning

Longlong Jing, Yucheng Chen, Ling Zhang +2

Most of the existing self-supervised feature learning methods for 3D data either learn 3D features from point cloud data or from multi-view images. By exploring the inherent multi-…

cs.CV20206 cited

Self-supervised Feature Learning by Cross-modality and Cross-view Correspondences

Longlong Jing, Yucheng Chen, Ling Zhang +2

The success of supervised learning requires large-scale ground truth labels which are very expensive, time-consuming, or may need special skills to annotate. To address this issue,…

cs.CV20191 cited

MSDC-Net: Multi-Scale Dense and Contextual Networks for Automated Disparity Map for Stereo Matching

Zhibo Rao, Mingyi He, Yuchao Dai +3

Disparity prediction from stereo images is essential to computer vision applications including autonomous driving, 3D model reconstruction, and object detection. To predict accurat…

cs.CV20191 cited

Multi-scale Cross-form Pyramid Network for Stereo Matching

Zhidong Zhu, Mingyi He, Yuchao Dai +2

Stereo matching plays an indispensable part in autonomous driving, robotics and 3D scene reconstruction. We propose a novel deep learning architecture, which called CFP-Net, a Cros…

cs.CV201717 cited

Deep Edge-Aware Saliency Detection

Jing Zhang, Yuchao Dai, Fatih Porikli +1

There has been profound progress in visual saliency thanks to the deep learning architectures, however, there still exist three major challenges that hinder the detection performan…