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

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

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6 papers · 1 filter

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.CV2018

Image Captioning based on Deep Reinforcement Learning

Haichao Shi, Peng Li, Bo Wang +1

Recently it has shown that the policy-gradient methods for reinforcement learning have been utilized to train deep end-to-end systems on natural language processing tasks. What's m…