Search Results for author: Zhengxiong Luo

Found 7 papers, 4 papers with code

Adaptive Dilated Convolution For Human Pose Estimation

no code implementations22 Jul 2021 Zhengxiong Luo, Zhicheng Wang, Yan Huang, Liang Wang, Tieniu Tan, Erjin Zhou

It can generate and fuse multi-scale features of the same spatial sizes by setting different dilation rates for different channels.

Pose Estimation

From General to Specific: Online Updating for Blind Super-Resolution

no code implementations6 Jul 2021 Shang Li, GuiXuan Zhang, Zhengxiong Luo, Jie Liu, Zhi Zeng, Shuwu Zhang

It does not rely on predefined blur kernels and allows the model weights to be updated according to the degradation of the test image.

Super-Resolution

End-to-end Alternating Optimization for Blind Super Resolution

1 code implementation14 May 2021 Zhengxiong Luo, Yan Huang, Shang Li, Liang Wang, Tieniu Tan

More importantly, \textit{Restorer} is trained with the kernel estimated by \textit{Estimator}, instead of the ground-truth kernel, thus \textit{Restorer} could be more tolerant to the estimation error of \textit{Estimator}.

Super-Resolution

Rethinking the Heatmap Regression for Bottom-up Human Pose Estimation

1 code implementation CVPR 2021 Zhengxiong Luo, Zhicheng Wang, Yan Huang, Tieniu Tan, Erjin Zhou

However, for bottom-up methods, which need to handle a large variance of human scales and labeling ambiguities, the current practice seems unreasonable.

Pose Estimation

Efficient Human Pose Estimation by Learning Deeply Aggregated Representations

no code implementations13 Dec 2020 Zhengxiong Luo, Zhicheng Wang, Yuanhao Cai, GuanAn Wang, Yan Huang, Liang Wang, Erjin Zhou, Tieniu Tan, Jian Sun

Instead, we focus on exploiting multi-scale information from layers with different receptive-field sizes and then making full of use this information by improving the fusion method.

Pose Estimation

Unfolding the Alternating Optimization for Blind Super Resolution

1 code implementation NeurIPS 2020 Zhengxiong Luo, Yan Huang, Shang Li, Liang Wang, Tieniu Tan

More importantly, \textit{Restorer} is trained with the kernel estimated by \textit{Estimator}, instead of ground-truth kernel, thus \textit{Restorer} could be more tolerant to the estimation error of \textit{Estimator}.

Super-Resolution

Learning Delicate Local Representations for Multi-Person Pose Estimation

3 code implementations ECCV 2020 Yuanhao Cai, Zhicheng Wang, Zhengxiong Luo, Binyi Yin, Angang Du, Haoqian Wang, Xiangyu Zhang, Xinyu Zhou, Erjin Zhou, Jian Sun

To tackle this problem, we propose an efficient attention mechanism - Pose Refine Machine (PRM) to make a trade-off between local and global representations in output features and further refine the keypoint locations.

Multi-Person Pose Estimation

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