activity
20182022
most citedExploiting Offset-guided Network for Pose Estimation and Tracking

14 citations · 21 across the 4 of their papers we have counts for

collaborators

9 papers

cs.CV2022

OPAL: Occlusion Pattern Aware Loss for Unsupervised Light Field Disparity Estimation

Peng Li, Jiayin Zhao, Jingyao Wu +3

Light field disparity estimation is an essential task in computer vision with various applications. Although supervised learning-based methods have achieved both higher accuracy an…

cs.CV20223 cited

Binary Neural Networks as a general-propose compute paradigm for on-device computer vision

Guhong Nie, Lirui Xiao, Menglong Zhu +6

For binary neural networks (BNNs) to become the mainstream on-device computer vision algorithm, they must achieve a superior speed-vs-accuracy tradeoff than 8-bit quantization and…

cs.CV2019

FastPose: Towards Real-time Pose Estimation and Tracking via Scale-normalized Multi-task Networks

Jiabin Zhang, Zheng Zhu, Wei Zou +4

Both accuracy and efficiency are significant for pose estimation and tracking in videos. State-of-the-art performance is dominated by two-stages top-down methods. Despite the leadi…

cs.CV20194 cited

State-aware Re-identification Feature for Multi-target Multi-camera Tracking

Peng Li, Jiabin Zhang, Zheng Zhu +3

Multi-target Multi-camera Tracking (MTMCT) aims to extract the trajectories from videos captured by a set of cameras. Recently, the tracking performance of MTMCT is significantly e…

cs.CV201914 cited

Exploiting Offset-guided Network for Pose Estimation and Tracking

Rui Zhang, Zheng Zhu, Peng Li +4

Human pose estimation has witnessed a significant advance thanks to the development of deep learning. Recent human pose estimation approaches tend to directly predict the location…

cs.LG2019

Robust Deep Multi-Modal Sensor Fusion using Fusion Weight Regularization and Target Learning

Myung Seok Shim, Chenye Zhao, Yang Li +3

Sensor fusion has wide applications in many domains including health care and autonomous systems. While the advent of deep learning has enabled promising multi-modal fusion of high…