Search Results for author: Zhiqiang Tang

Found 11 papers, 5 papers with code

Enabling Data Diversity: Efficient Automatic Augmentation via Regularized Adversarial Training

1 code implementation30 Mar 2021 Yunhe Gao, Zhiqiang Tang, Mu Zhou, Dimitris Metaxas

Data augmentation has proved extremely useful by increasing training data variance to alleviate overfitting and improve deep neural networks' generalization performance.

Data Augmentation Skin Cancer Classification

CrossNorm and SelfNorm for Generalization under Distribution Shifts

1 code implementation ICCV 2021 Zhiqiang Tang, Yunhe Gao, Yi Zhu, Zhi Zhang, Mu Li, Dimitris Metaxas

Can we develop new normalization methods to improve generalization robustness under distribution shifts?

Unity of Opposites: SelfNorm and CrossNorm for Model Robustness

no code implementations1 Jan 2021 Zhiqiang Tang, Yunhe Gao, Yi Zhu, Zhi Zhang, Mu Li, Dimitris N. Metaxas

CrossNorm exchanges styles between feature channels to perform style augmentation, diversifying the content and style mixtures.

Object Recognition Unity

Semantic-Guided Multi-Attention Localization for Zero-Shot Learning

no code implementations NeurIPS 2019 Yizhe Zhu, Jianwen Xie, Zhiqiang Tang, Xi Peng, Ahmed Elgammal

Zero-shot learning extends the conventional object classification to the unseen class recognition by introducing semantic representations of classes.

Zero-Shot Learning

Quantized Densely Connected U-Nets for Efficient Landmark Localization

1 code implementation ECCV 2018 Zhiqiang Tang, Xi Peng, Shijie Geng, Lingfei Wu, Shaoting Zhang, Dimitris Metaxas

Finally, to reduce the memory consumption and high precision operations both in training and testing, we further quantize weights, inputs, and gradients of our localization network to low bit-width numbers.

Face Alignment Pose Estimation

Toward Marker-free 3D Pose Estimation in Lifting: A Deep Multi-view Solution

no code implementations6 Feb 2018 Rahil Mehrizi, Xi Peng, Zhiqiang Tang, Xu Xu, Dimitris Metaxas, Kang Li

The results are also compared with state-of-the-art methods on HumanEva-I dataset, which demonstrates the superior performance of our approach.

3D Pose Estimation

Vision-based Robotic Arm Imitation by Human Gesture

no code implementations15 Mar 2017 Cheng Xuan, Zhiqiang Tang, Jinxin Xu

One of the most efficient ways for a learning-based robotic arm to learn to process complex tasks as human, is to directly learn from observing how human complete those tasks, and then imitate.

Robotics

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