Search Results for author: Terrence Chen

Found 24 papers, 0 papers with code

Learning Local Recurrent Models for Human Mesh Recovery

no code implementations27 Jul 2021 Runze Li, Srikrishna Karanam, Ren Li, Terrence Chen, Bir Bhanu, Ziyan Wu

We conduct a variety of experiments on standard video mesh recovery benchmark datasets such as Human3. 6M, MPI-INF-3DHP, and 3DPW, demonstrating the efficacy of our design of modeling local dynamics as well as establishing state-of-the-art results based on standard evaluation metrics.

Spatio-Temporal Representation Factorization for Video-based Person Re-Identification

no code implementations ICCV 2021 Abhishek Aich, Meng Zheng, Srikrishna Karanam, Terrence Chen, Amit K. Roy-Chowdhury, Ziyan Wu

To alleviate these problems, we propose Spatio-Temporal Representation Factorization (STRF), a flexible new computational unit that can be used in conjunction with most existing 3D convolutional neural network architectures for re-ID.

Video-Based Person Re-Identification

Everybody Is Unique: Towards Unbiased Human Mesh Recovery

no code implementations13 Jul 2021 Ren Li, Meng Zheng, Srikrishna Karanam, Terrence Chen, Ziyan Wu

Next, we present a simple baseline to address this problem that is scalable and can be easily used in conjunction with existing algorithms to improve their performance.

Multi-scale Neural ODEs for 3D Medical Image Registration

no code implementations16 Jun 2021 Junshen Xu, Eric Z. Chen, Xiao Chen, Terrence Chen, Shanhui Sun

The inference consists of iterative gradient updates similar to a conventional gradient descent optimizer but in a much faster way, because the neural ODE learns from the training data to adapt the gradient efficiently at each iteration.

Image Registration Medical Image Registration

Accelerating 3D MULTIPLEX MRI Reconstruction with Deep Learning

no code implementations17 May 2021 Eric Z. Chen, Yongquan Ye, Xiao Chen, Jingyuan Lyu, Zhongqi Zhang, Yichen Hu, Terrence Chen, Jian Xu, Shanhui Sun

We propose a deep learning framework for undersampled 3D MRI data reconstruction and apply it to MULTIPLEX MRI.

MRI Reconstruction

Cardiac Functional Analysis with Cine MRI via Deep Learning Reconstruction

no code implementations17 May 2021 Eric Z. Chen, Xiao Chen, Jingyuan Lyu, Qi Liu, Zhongqi Zhang, Yu Ding, Shuheng Zhang, Terrence Chen, Jian Xu, Shanhui Sun

To the best of our knowledge, this is the first work to evaluate the cine MRI with deep learning reconstruction for cardiac function analysis and compare it with other conventional methods.

Ensemble Attention Distillation for Privacy-Preserving Federated Learning

no code implementations ICCV 2021 Xuan Gong, Abhishek Sharma, Srikrishna Karanam, Ziyan Wu, Terrence Chen, David Doermann, Arun Innanje

Such decentralized training naturally leads to issues of imbalanced or differing data distributions among the local models and challenges in fusing them into a central model.

Federated Learning

Measure Anatomical Thickness from Cardiac MRI with Deep Neural Networks

no code implementations25 Aug 2020 Qiaoying Huang, Eric Z. Chen, Hanchao Yu, Yimo Guo, Terrence Chen, Dimitris Metaxas, Shanhui Sun

We also analyze thickness patterns on different cardiac pathologies with a standard clinical model and the results demonstrate the potential clinical value of our method for thickness based cardiac disease diagnosis.

Anatomy-Aware Cardiac Motion Estimation

no code implementations17 Aug 2020 Pingjun Chen, Xiao Chen, Eric Z. Chen, Hanchao Yu, Terrence Chen, Shanhui Sun

A baseline dense motion tracker is trained to approximate the motion fields and then refined to estimate anatomy-aware motion fields under the weak supervision from the VAE.

Motion Estimation

Towards Visually Explaining Similarity Models

no code implementations13 Aug 2020 Meng Zheng, Srikrishna Karanam, Terrence Chen, Richard J. Radke, Ziyan Wu

We show that the resulting similarity models perform, and can be visually explained, better than the corresponding baseline models trained without these constraints.

Image Retrieval Metric Learning +2

Real-Time Cardiac Cine MRI with Residual Convolutional Recurrent Neural Network

no code implementations12 Aug 2020 Eric Z. Chen, Xiao Chen, Jingyuan Lyu, Yuan Zheng, Terrence Chen, Jian Xu, Shanhui Sun

Real-time cardiac cine MRI does not require ECG gating in the data acquisition and is more useful for patients who can not hold their breaths or have abnormal heart rhythms.

Image Reconstruction

Motion Pyramid Networks for Accurate and Efficient Cardiac Motion Estimation

no code implementations28 Jun 2020 Hanchao Yu, Xiao Chen, Humphrey Shi, Terrence Chen, Thomas S. Huang, Shanhui Sun

In this paper, we propose Motion Pyramid Networks, a novel deep learning-based approach for accurate and efficient cardiac motion estimation.

Knowledge Distillation Motion Estimation

MRI Image Reconstruction via Learning Optimization Using Neural ODEs

no code implementations24 Jun 2020 Eric Z. Chen, Terrence Chen, Shanhui Sun

We investigate several models based on three ODE solvers and compare models with fixed solvers and learned solvers.

MRI Reconstruction

FOAL: Fast Online Adaptive Learning for Cardiac Motion Estimation

no code implementations CVPR 2020 Hanchao Yu, Shanhui Sun, Haichao Yu, Xiao Chen, Honghui Shi, Thomas Huang, Terrence Chen

In clinical deployment, however, they suffer dramatic performance drops due to mismatched distributions between training and testing datasets, commonly encountered in the clinical environment.

Motion Estimation

Hierarchical Kinematic Human Mesh Recovery

no code implementations ECCV 2020 Georgios Georgakis, Ren Li, Srikrishna Karanam, Terrence Chen, Jana Kosecka, Ziyan Wu

In this work, we address this gap by proposing a new technique for regression of human parametric model that is explicitly informed by the known hierarchical structure, including joint interdependencies of the model.

Hierarchical structure

Pyramid Convolutional RNN for MRI Reconstruction

no code implementations2 Dec 2019 Puyang Wang, Eric Z. Chen, Terrence Chen, Vishal M. Patel, Shanhui Sun

Fast and accurate MRI image reconstruction from undersampled data is critically important in clinical practice.

MRI Reconstruction

Structure-Aware Shape Synthesis

no code implementations4 Aug 2018 Elena Balashova, Vivek Singh, Jiangping Wang, Brian Teixeira, Terrence Chen, Thomas Funkhouser

We propose a new procedure to guide training of a data-driven shape generative model using a structure-aware loss function.

Generating Synthetic X-ray Images of a Person from the Surface Geometry

no code implementations CVPR 2018 Brian Teixeira, Vivek Singh, Terrence Chen, Kai Ma, Birgi Tamersoy, Yifan Wu, Elena Balashova, Dorin Comaniciu

Furthermore, the synthetic X-ray image is parametrized and can be manipulated by adjusting a set of body markers which are also generated during the X-ray image prediction.

Anomaly Detection

Deep Decision Network for Multi-Class Image Classification

no code implementations CVPR 2016 Venkatesh N. Murthy, Vivek Singh, Terrence Chen, R. Manmatha, Dorin Comaniciu

During the learning phase, starting from the root network node, DDN automatically builds a network that splits the data into disjoint clusters of classes which would be handled by the subsequent expert networks.

Classification General Classification +1

BodyPrint: Pose Invariant 3D Shape Matching of Human Bodies

no code implementations ICCV 2015 Jiangping Wang, Kai Ma, Vivek Kumar Singh, Thomas Huang, Terrence Chen

3D human body shape matching has large potential on many real world applications, especially with the recent advances in the 3D range sensing technology.

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