b'Xiaofeng Liu'

research

∙ 09/16/2022

Memory Consistent Unsupervised Off-the-Shelf Model Adaptation for Source-Relaxed Medical Image Segmentation

Unsupervised domain adaptation (UDA) has been a vital protocol for migra...

Xiaofeng Liu, et al. ∙

research

∙ 08/26/2022

Constraining Pseudo-label in Self-training Unsupervised Domain Adaptation with Energy-based Model

Deep learning is usually data starved, and the unsupervised domain adapt...

Lingsheng Kong, et al. ∙

research

∙ 08/16/2022

Unsupervised Domain Adaptation for Segmentation with Black-box Source Model

Unsupervised domain adaptation (UDA) has been widely used to transfer kn...

Xiaofeng Liu, et al. ∙

research

∙ 08/16/2022

Subtype-Aware Dynamic Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) has been successfully applied to tr...

Xiaofeng Liu, et al. ∙

research

∙ 08/15/2022

Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectives

Deep learning has become the method of choice to tackle real-world probl...

Xiaofeng Liu, et al. ∙

research

∙ 06/05/2022

ACT: Semi-supervised Domain-adaptive Medical Image Segmentation with Asymmetric Co-training

Unsupervised domain adaptation (UDA) has been vastly explored to allevia...

Xiaofeng Liu, et al. ∙

research

∙ 06/05/2022

Tagged-MRI Sequence to Audio Synthesis via Self Residual Attention Guided Heterogeneous Translator

Understanding the underlying relationship between tongue and oropharynge...

Xiaofeng Liu, et al. ∙

research

∙ 03/25/2022

Sparse Federated Learning with Hierarchical Personalization Models

Federated learning (FL) is widely used in the Internet of Things (IoT), ...

Xiaofeng Liu, et al. ∙

research

∙ 03/05/2022

Bathymetry Inversion using a Deep-Learning-Based Surrogate for Shallow Water Equations Solvers

River bathymetry is critical for many aspects of water resources managem...

Xiaofeng Liu, et al. ∙

research

∙ 02/25/2022

Structure-aware Unsupervised Tagged-to-Cine MRI Synthesis with Self Disentanglement

Cycle reconstruction regularized adversarial training – e.g., CycleGAN, ...

Xiaofeng Liu, et al. ∙

research

∙ 01/22/2022

A Multi-modal Fusion Framework Based on Multi-task Correlation Learning for Cancer Prognosis Prediction

Morphological attributes from histopathological images and molecular pro...

Kaiwen Tan, et al. ∙

research

∙ 01/18/2022

Variational Inference for Quantifying Inter-observer Variability in Segmentation of Anatomical Structures

Lesions or organ boundaries visible through medical imaging data are oft...

Xiaofeng Liu, et al. ∙

research

∙ 01/13/2022

Self-semantic contour adaptation for cross modality brain tumor segmentation

Unsupervised domain adaptation (UDA) between two significantly disparate...

Xiaofeng Liu, et al. ∙

research

∙ 12/20/2021

Surrogate Model for Shallow Water Equations Solvers with Deep Learning

Shallow water equations are the foundation of most models for flooding a...

Yalan Song, et al. ∙

research

∙ 07/28/2021

Adversarial Unsupervised Domain Adaptation with Conditional and Label Shift: Infer, Align and Iterate

In this work, we propose an adversarial unsupervised domain adaptation (...

Xiaofeng Liu, et al. ∙

research

∙ 07/28/2021

Recursively Conditional Gaussian for Ordinal Unsupervised Domain Adaptation

There has been a growing interest in unsupervised domain adaptation (UDA...

Xiaofeng Liu, et al. ∙

research

∙ 07/22/2021

Domain Generalization under Conditional and Label Shifts via Variational Bayesian Inference

In this work, we propose a domain generalization (DG) approach to learn ...

Xiaofeng Liu, et al. ∙

research

∙ 07/22/2021

Segmentation of Cardiac Structures via Successive Subspace Learning with Saab Transform from Cine MRI

Assessment of cardiovascular disease (CVD) with cine magnetic resonance ...

Xiaofeng Liu, et al. ∙

research

∙ 07/22/2021

Deep 3D-CNN for Depression Diagnosis with Facial Video Recording of Self-Rating Depression Scale Questionnaire

The Self-Rating Depression Scale (SDS) questionnaire is commonly utilize...

Wanqing Xie, et al. ∙

research

∙ 06/23/2021

Generative Self-training for Cross-domain Unsupervised Tagged-to-Cine MRI Synthesis

Self-training based unsupervised domain adaptation (UDA) has shown great...

Xiaofeng Liu, et al. ∙

research

∙ 06/23/2021

Adapting Off-the-Shelf Source Segmenter for Target Medical Image Segmentation

Unsupervised domain adaptation (UDA) aims to transfer knowledge learned ...

Xiaofeng Liu, et al. ∙

research

∙ 04/30/2021

Embedding Semantic Hierarchy in Discrete Optimal Transport for Risk Minimization

The widely-used cross-entropy (CE) loss-based deep networks achieved sig...

Yubin Ge, et al. ∙

research

∙ 04/09/2021

Language model fusion for streaming end to end speech recognition

Streaming processing of speech audio is required for many contemporary p...

Rodrigo Cabrera, et al. ∙

research

∙ 01/17/2021

Symmetric-Constrained Irregular Structure Inpainting for Brain MRI Registration with Tumor Pathology

Deformable registration of magnetic resonance images between patients wi...

Xiaofeng Liu, et al. ∙

research

∙ 01/14/2021

Dual-cycle Constrained Bijective VAE-GAN For Tagged-to-Cine Magnetic Resonance Image Synthesis

Tagged magnetic resonance imaging (MRI) is a widely used imaging techniq...

Xiaofeng Liu, et al. ∙

research

∙ 01/14/2021

A Unified Conditional Disentanglement Framework for Multimodal Brain MR Image Translation

Multimodal MRI provides complementary and clinically relevant informatio...

Xiaofeng Liu, et al. ∙

research

∙ 01/13/2021

VoxelHop: Successive Subspace Learning for ALS Disease Classification Using Structural MRI

Deep learning has great potential for accurate detection and classificat...

Xiaofeng Liu, et al. ∙

research

∙ 01/01/2021

Subtype-aware Unsupervised Domain Adaptation for Medical Diagnosis

Recent advances in unsupervised domain adaptation (UDA) show that transf...

Xiaofeng Liu, et al. ∙

research

∙ 01/01/2021

Identity-aware Facial Expression Recognition in Compressed Video

This paper targets to explore the inter-subject variations eliminated fa...

Xiaofeng Liu, et al. ∙

research

∙ 01/01/2021

Energy-constrained Self-training for Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) aims to transfer the knowledge on a...

Xiaofeng Liu, et al. ∙

research

∙ 10/21/2020

Importance-Aware Semantic Segmentation in Self-Driving with Discrete Wasserstein Training

Semantic segmentation (SS) is an important perception manner for self-dr...

Xiaofeng Liu, et al. ∙

research

∙ 10/20/2020

Mutual Information Regularized Identity-aware Facial ExpressionRecognition in Compressed Video

This paper targets to explore the inter-subject variations eliminated fa...

Xiaofeng Liu, et al. ∙

research

∙ 08/11/2020

Reinforced Wasserstein Training for Severity-Aware Semantic Segmentation in Autonomous Driving

Semantic segmentation is important for many real-world systems, e.g., au...

Xiaofeng Liu, et al. ∙

research

∙ 07/13/2020

AUTO3D: Novel view synthesis through unsupervisely learned variational viewpoint and global 3D representation

This paper targets on learning-based novel view synthesis from a single ...

Xiaofeng Liu, et al. ∙

research

∙ 06/14/2020

Disentanglement for Discriminative Visual Recognition

Recent successes of deep learning-based recognition rely on maintaining ...

Xiaofeng Liu, et al. ∙

research

∙ 02/04/2020

TPPO: A Novel Trajectory Predictor with Pseudo Oracle

Forecasting pedestrian trajectories in dynamic scenes remains a critical...

Biao Yang, et al. ∙

research

∙ 02/02/2020

A Novel Graph based Trajectory Predictor with Pseudo Oracle

Pedestrian trajectory prediction in dynamic scenes remains a challenging...

Biao Yang, et al. ∙

research

∙ 12/12/2019

Towards Disentangled Representations for Human Retargeting by Multi-view Learning

We study the problem of learning disentangled representations for data a...

Chao Yang, et al. ∙

research

∙ 11/18/2019

Deep Verifier Networks: Verification of Deep Discriminative Models with Deep Generative Models

AI Safety is a major concern in many deep learning applications such as ...

Tong Che, et al. ∙

research

∙ 11/03/2019

Unimodal-uniform Constrained Wasserstein Training for Medical Diagnosis

The labels in medical diagnosis task are usually discrete and successive...

Xiaofeng Liu, et al. ∙

research

∙ 11/03/2019

Conservative Wasserstein Training for Pose Estimation

This paper targets the task with discrete and periodic class labels (e.g...

Xiaofeng Liu, et al. ∙

research

∙ 08/26/2019

Confidence Regularized Self-Training

Recent advances in domain adaptation show that deep self-training presen...

Yang Zou, et al. ∙

research

∙ 08/05/2019

Attention Control with Metric Learning Alignment for Image Set-based Recognition

This paper considers the problem of image set-based face verification an...

Xiaofeng Liu, et al. ∙

research

∙ 08/03/2019

Permutation-invariant Feature Restructuring for Correlation-aware Image Set-based Recognition

We consider the problem of comparing the similarity of image sets with v...

Xiaofeng Liu, et al. ∙

research

∙ 07/05/2019

Dependency-aware Attention Control for Unconstrained Face Recognition with Image Sets

This paper targets the problem of image set-based face verification and ...

Xiaofeng Liu, et al. ∙

research

∙ 11/26/2018

Control with Distributed Deep Reinforcement Learning: Learn a Better Policy

Distributed approach is a very effective method to improve training effi...

Qihao Liu, et al. ∙

research

∙ 03/23/2018

Image Inpainting using Block-wise Procedural Training with Annealed Adversarial Counterpart

Recent advances in deep generative models have shown promising potential...

Chao Yang, et al. ∙

research

∙ 03/23/2018

Image Inpainting using Block-wise Procedural Training with Annealed Adversarial Counterpar

Recent advances in deep generative models have shown promising potential...

Chao Yang, et al. ∙

research

∙ 06/10/2015

Fast Online Clustering with Randomized Skeleton Sets

We present a new fast online clustering algorithm that reliably recovers...

Krzysztof Choromanski, et al. ∙

Xiaofeng Liu

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