207 citations · 466 across the 18 of their papers we have counts for
19 papers
Dual-Decoder Consistency via Pseudo-Labels Guided Data Augmentation for Semi-Supervised Medical Image Segmentation
Yuanbin Chen, Tao Wang, Hui Tang +6
While supervised learning has achieved remarkable success, obtaining large-scale labeled datasets in biomedical imaging is often impractical due to high costs and the time-consumin…
MEGAN: Memory Enhanced Graph Attention Network for Space-Time Video Super-Resolution
Chenyu You, Lianyi Han, Aosong Feng +3
Space-time video super-resolution (STVSR) aims to construct a high space-time resolution video sequence from the corresponding low-frame-rate, low-resolution video sequence. Inspir…
Geometry-Aware Self-Training for Unsupervised Domain Adaptationon Object Point Clouds
Longkun Zou, Hui Tang, Ke Chen +1
The point cloud representation of an object can have a large geometric variation in view of inconsistent data acquisition procedure, which thus leads to domain discrepancy due to d…
Object-Guided Instance Segmentation With Auxiliary Feature Refinement for Biological Images
Jingru Yi, Pengxiang Wu, Hui Tang +7
Instance segmentation is of great importance for many biological applications, such as study of neural cell interactions, plant phenotyping, and quantitatively measuring how cells…
On Universal Black-Box Domain Adaptation
Bin Deng, Yabin Zhang, Hui Tang +2
In this paper, we study an arguably least restrictive setting of domain adaptation in a sense of practical deployment, where only the interface of source model is available to the…
Vicinal and categorical domain adaptation
Hui Tang, Kui Jia
Unsupervised domain adaptation aims to learn a task classifier that performs well on the unlabeled target domain, by utilizing the labeled source domain. Inspiring results have bee…
Towards Uncovering the Intrinsic Data Structures for Unsupervised Domain Adaptation using Structurally Regularized Deep Clustering
Hui Tang, Xiatian Zhu, Ke Chen +2
Unsupervised domain adaptation (UDA) is to learn classification models that make predictions for unlabeled data on a target domain, given labeled data on a source domain whose dist…
Bipartite Distance for Shape-Aware Landmark Detection in Spinal X-Ray Images
Abdullah-Al-Zubaer Imran, Chao Huang, Hui Tang +5
Scoliosis is a congenital disease that causes lateral curvature in the spine. Its assessment relies on the identification and localization of vertebrae in spinal X-ray images, conv…
Partly Supervised Multitask Learning
Abdullah-Al-Zubaer Imran, Chao Huang, Hui Tang +5
Semi-supervised learning has recently been attracting attention as an alternative to fully supervised models that require large pools of labeled data. Moreover, optimizing a model…
Analysis of Scoliosis From Spinal X-Ray Images
Abdullah-Al-Zubaer Imran, Chao Huang, Hui Tang +5
Scoliosis is a congenital disease in which the spine is deformed from its normal shape. Measurement of scoliosis requires labeling and identification of vertebrae in the spine. Spi…
Unsupervised Domain Adaptation via Structurally Regularized Deep Clustering
Hui Tang, Ke Chen, Kui Jia
Unsupervised domain adaptation (UDA) is to make predictions for unlabeled data on a target domain, given labeled data on a source domain whose distribution shifts from the target o…
Unsupervised Multi-Class Domain Adaptation: Theory, Algorithms, and Practice
Yabin Zhang, Bin Deng, Hui Tang +2
In this paper, we study the formalism of unsupervised multi-class domain adaptation (multi-class UDA), which underlies a few recent algorithms whose learning objectives are only mo…
Discriminative Adversarial Domain Adaptation
Hui Tang, Kui Jia
Given labeled instances on a source domain and unlabeled ones on a target domain, unsupervised domain adaptation aims to learn a task classifier that can well classify target insta…
Shape-Aware Organ Segmentation by Predicting Signed Distance Maps
Yuan Xue, Hui Tang, Zhi Qiao +6
In this work, we propose to resolve the issue existing in current deep learning based organ segmentation systems that they often produce results that do not capture the overall sha…
Object-Guided Instance Segmentation for Biological Images
Jingru Yi, Hui Tang, Pengxiang Wu +5
Instance segmentation of biological images is essential for studying object behaviors and properties. The challenges, such as clustering, occlusion, and adhesion problems of the ob…
Domain-Symmetric Networks for Adversarial Domain Adaptation
Yabin Zhang, Hui Tang, Kui Jia +1
Unsupervised domain adaptation aims to learn a model of classifier for unlabeled samples on the target domain, given training data of labeled samples on the source domain. Impressi…
Generating Realistic Training Images Based on Tonality-Alignment Generative Adversarial Networks for Hand Pose Estimation
Liangjian Chen, Shih-Yao Lin, Yusheng Xie +5
Hand pose estimation from a monocular RGB image is an important but challenging task. The main factor affecting its performance is the lack of a sufficiently large training dataset…
3D Segmentation with Exponential Logarithmic Loss for Highly Unbalanced Object Sizes
Ken C. L. Wong, Mehdi Moradi, Hui Tang +1
With the introduction of fully convolutional neural networks, deep learning has raised the benchmark for medical image segmentation on both speed and accuracy, and different networ…
Fine-Grained Visual Categorization using Meta-Learning Optimization with Sample Selection of Auxiliary Data
Yabin Zhang, Hui Tang, Kui Jia
Fine-grained visual categorization (FGVC) is challenging due in part to the fact that it is often difficult to acquire an enough number of training samples. To employ large models…