Search Results for author: Wentao Zhu

Found 35 papers, 12 papers with code

Adversarial Contrastive Self-Supervised Learning

no code implementations26 Feb 2022 Wentao Zhu, Hang Shang, Tingxun Lv, Chao Liao, Sen yang, Ji Liu

Recently, learning from vast unlabeled data, especially self-supervised learning, has been emerging and attracted widespread attention.

Self-Supervised Learning

Associative Adversarial Learning Based on Selective Attack

no code implementations28 Dec 2021 Runqi Wang, Xiaoyue Duan, Baochang Zhang, Song Xue, Wentao Zhu, David Doermann, Guodong Guo

We show that our method improves the recognition accuracy of adversarial training on ImageNet by 8. 32% compared with the baseline.

Adversarial Robustness Few-Shot Learning +2

MoCaNet: Motion Retargeting in-the-wild via Canonicalization Networks

no code implementations19 Dec 2021 Wentao Zhu, Zhuoqian Yang, Ziang Di, Wayne Wu, Yizhou Wang, Chen Change Loy

Trained with the canonicalization operations and the derived regularizations, our method learns to factorize a skeleton sequence into three independent semantic subspaces, i. e., motion, structure, and view angle.

3D Reconstruction Action Analysis +1

Self-Supervised Monocular Depth and Ego-Motion Estimation in Endoscopy: Appearance Flow to the Rescue

1 code implementation15 Dec 2021 Shuwei Shao, Zhongcai Pei, Weihai Chen, Wentao Zhu, Xingming Wu, Dianmin Sun, Baochang Zhang

Recently, self-supervised learning technology has been applied to calculate depth and ego-motion from monocular videos, achieving remarkable performance in autonomous driving scenarios.

Depth Estimation Motion Estimation +1

NENet: Monocular Depth Estimation via Neural Ensembles

no code implementations16 Nov 2021 Shuwei Shao, Ran Li, Zhongcai Pei, Zhong Liu, Weihai Chen, Wentao Zhu, Xingming Wu, Baochang Zhang

In particular, our method improves previous state-of-the-art methods from 0. 365 to 0. 349 on the metric RMSE on the NYU dataset.

Ensemble Learning Monocular Depth Estimation

Joint Channel and Weight Pruning for Model Acceleration on Moblie Devices

1 code implementation15 Oct 2021 Tianli Zhao, Xi Sheryl Zhang, Wentao Zhu, Jiaxing Wang, Sen yang, Ji Liu, Jian Cheng

In this paper, we present a unified framework with Joint Channel pruning and Weight pruning (JCW), and achieves a better Pareto-frontier between the latency and accuracy than previous model compression approaches.

Model Compression

SpeechNAS: Towards Better Trade-off between Latency and Accuracy for Large-Scale Speaker Verification

1 code implementation18 Sep 2021 Wentao Zhu, Tianlong Kong, Shun Lu, Jixiang Li, Dawei Zhang, Feng Deng, Xiaorui Wang, Sen yang, Ji Liu

Recently, x-vector has been a successful and popular approach for speaker verification, which employs a time delay neural network (TDNN) and statistics pooling to extract speaker characterizing embedding from variable-length utterances.

Neural Architecture Search Speaker Recognition +2

Shifted Chunk Transformer for Spatio-Temporal Representational Learning

no code implementations NeurIPS 2021 Xuefan Zha, Wentao Zhu, Tingxun Lv, Sen yang, Ji Liu

However, the pure-Transformer based spatio-temporal learning can be prohibitively costly on memory and computation to extract fine-grained features from a tiny patch.

Action Anticipation Action Recognition +5

Test-Time Training for Deformable Multi-Scale Image Registration

no code implementations25 Mar 2021 Wentao Zhu, Yufang Huang, Daguang Xu, Zhen Qian, Wei Fan, Xiaohui Xie

Registration is a fundamental task in medical robotics and is often a crucial step for many downstream tasks such as motion analysis, intra-operative tracking and image segmentation.

Image Registration Semantic Segmentation

Multi-Domain Image Completion for Random Missing Input Data

no code implementations10 Jul 2020 Liyue Shen, Wentao Zhu, Xiaosong Wang, Lei Xing, John M. Pauly, Baris Turkbey, Stephanie Anne Harmon, Thomas Hogue Sanford, Sherif Mehralivand, Peter Choyke, Bradford Wood, Daguang Xu

Multi-domain data are widely leveraged in vision applications taking advantage of complementary information from different modalities, e. g., brain tumor segmentation from multi-parametric magnetic resonance imaging (MRI).

Brain Tumor Segmentation Disentanglement +1

LAMP: Large Deep Nets with Automated Model Parallelism for Image Segmentation

1 code implementation22 Jun 2020 Wentao Zhu, Can Zhao, Wenqi Li, Holger Roth, Ziyue Xu, Daguang Xu

In this work, we introduce Large deep 3D ConvNets with Automated Model Parallelism (LAMP) and investigate the impact of both input's and deep 3D ConvNets' size on segmentation accuracy.

Semantic Segmentation

TransMoMo: Invariance-Driven Unsupervised Video Motion Retargeting

no code implementations CVPR 2020 Zhuoqian Yang, Wentao Zhu, Wayne Wu, Chen Qian, Qiang Zhou, Bolei Zhou, Chen Change Loy

We present a lightweight video motion retargeting approach TransMoMo that is capable of transferring motion of a person in a source video realistically to another video of a target person.

motion retargeting

NeurReg: Neural Registration and Its Application to Image Segmentation

1 code implementation4 Oct 2019 Wentao Zhu, Andriy Myronenko, Ziyue Xu, Wenqi Li, Holger Roth, Yufang Huang, Fausto Milletari, Daguang Xu

Furthermore, we design three segmentation frameworks based on the proposed registration framework: 1) atlas-based segmentation, 2) joint learning of both segmentation and registration tasks, and 3) multi-task learning with atlas-based segmentation as an intermediate feature.

Image Registration Multi-Task Learning +1

Cardiac Segmentation of LGE MRI with Noisy Labels

no code implementations2 Oct 2019 Holger Roth, Wentao Zhu, Dong Yang, Ziyue Xu, Daguang Xu

In the first step, we register a small set of five LGE cardiac magnetic resonance (CMR) images with ground truth labels to a set of 40 target LGE CMR images without annotation.

Cardiac Segmentation Data Augmentation +1

Neural Multi-Scale Self-Supervised Registration for Echocardiogram Dense Tracking

no code implementations18 Jun 2019 Wentao Zhu, Yufang Huang, Mani A. Vannan, Shizhen Liu, Daguang Xu, Wei Fan, Zhen Qian, Xiaohui Xie

In this work, we propose a neural multi-scale self-supervised registration (NMSR) method for automated myocardial and cardiac blood flow dense tracking.

Deep Learning for Automated Medical Image Analysis

no code implementations12 Mar 2019 Wentao Zhu

Second, we will demonstrate how to use the weakly labeled data for the mammogram breast cancer diagnosis by efficiently design deep learning for multi-instance learning.

Lung Nodule Detection

AnatomyNet: Deep Learning for Fast and Fully Automated Whole-volume Segmentation of Head and Neck Anatomy

2 code implementations15 Aug 2018 Wentao Zhu, Yufang Huang, Liang Zeng, Xuming Chen, Yong liu, Zhen Qian, Nan Du, Wei Fan, Xiaohui Xie

Methods: Our deep learning model, called AnatomyNet, segments OARs from head and neck CT images in an end-to-end fashion, receiving whole-volume HaN CT images as input and generating masks of all OARs of interest in one shot.

3D Medical Imaging Segmentation

DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification

1 code implementation25 Jan 2018 Wentao Zhu, Chaochun Liu, Wei Fan, Xiaohui Xie

DeepLung consists of two components, nodule detection (identifying the locations of candidate nodules) and classification (classifying candidate nodules into benign or malignant).

Classification Computed Tomography (CT) +2

Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification

1 code implementation23 May 2017 Wentao Zhu, Qi Lou, Yeeleng Scott Vang, Xiaohui Xie

Inspired by the success of using deep convolutional features for natural image analysis and multi-instance learning (MIL) for labeling a set of instances/patches, we propose end-to-end trained deep multi-instance networks for mass classification based on whole mammogram without the aforementioned ROIs.

Classification General Classification +1

Leak Event Identification in Water Systems Using High Order CRF

no code implementations12 Mar 2017 Qing Han, Wentao Zhu, Yang Shi

Today, detection of anomalous events in civil infrastructures (e. g. water pipe breaks and leaks) is time consuming and often takes hours or days.

Adversarial Deep Structural Networks for Mammographic Mass Segmentation

1 code implementation18 Dec 2016 Wentao Zhu, Xiang Xiang, Trac. D. Tran, Xiaohui Xie

Experimental results on two public datasets, INbreast and DDSM-BCRP, show that our end-to-end network combined with adversarial training achieves the-state-of-the-art results.

Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification

no code implementations18 Dec 2016 Wentao Zhu, Qi Lou, Yeeleng Scott Vang, Xiaohui Xie

Inspired by the success of using deep convolutional features for natural image analysis and multi-instance learning for labeling a set of instances/patches, we propose end-to-end trained deep multi-instance networks for mass classification based on whole mammogram without the aforementioned costly need to annotate the training data.

Classification General Classification +1

Co-occurrence Feature Learning for Skeleton based Action Recognition using Regularized Deep LSTM Networks

no code implementations24 Mar 2016 Wentao Zhu, Cuiling Lan, Junliang Xing, Wen-Jun Zeng, Yanghao Li, Li Shen, Xiaohui Xie

Skeleton based action recognition distinguishes human actions using the trajectories of skeleton joints, which provide a very good representation for describing actions.

Action Recognition Skeleton Based Action Recognition

Deep Trans-layer Unsupervised Networks for Representation Learning

no code implementations27 Sep 2015 Wentao Zhu, Jun Miao, Laiyun Qing, Xilin Chen

Compared to traditional deep learning methods, the implemented feature learning method has much less parameters and is validated in several typical experiments, such as digit recognition on MNIST and MNIST variations, object recognition on Caltech 101 dataset and face verification on LFW dataset.

Face Verification Object Recognition +1

Constrained Extreme Learning Machines: A Study on Classification Cases

1 code implementation25 Jan 2015 Wentao Zhu, Jun Miao, Laiyun Qing

Extreme learning machine (ELM) is an extremely fast learning method and has a powerful performance for pattern recognition tasks proven by enormous researches and engineers.

Classification General Classification

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