Search Results for author: Xiaofeng Liu

Found 82 papers, 9 papers with code

Why does Knowledge Distillation Work? Rethink its Attention and Fidelity Mechanism

1 code implementation30 Apr 2024 Chenqi Guo, Shiwei Zhong, Xiaofeng Liu, Qianli Feng, Yinglong Ma

By increasing data augmentation strengths, our key findings reveal a decrease in the Intersection over Union (IoU) of attentions between teacher models, leading to reduced student overfitting and decreased fidelity.

Data Augmentation Knowledge Distillation +1

Treatment-wise Glioblastoma Survival Inference with Multi-parametric Preoperative MRI

no code implementations10 Feb 2024 Xiaofeng Liu, Nadya Shusharina, Helen A Shih, C. -C. Jay Kuo, Georges El Fakhri, Jonghye Woo

In this work, we aim to predict the survival time (ST) of glioblastoma (GBM) patients undergoing different treatments based on preoperative magnetic resonance (MR) scans.

Speech motion anomaly detection via cross-modal translation of 4D motion fields from tagged MRI

no code implementations10 Feb 2024 Xiaofeng Liu, Fangxu Xing, Jiachen Zhuo, Maureen Stone, Jerry L. Prince, Georges El Fakhri, Jonghye Woo

In this work, we aim to develop a framework for detecting speech motion anomalies in conjunction with their corresponding speech acoustics.

Anomaly Detection

Disentangled Multimodal Brain MR Image Translation via Transformer-based Modality Infuser

no code implementations1 Feb 2024 Jihoon Cho, Xiaofeng Liu, Fangxu Xing, Jinsong Ouyang, Georges El Fakhri, Jinah Park, Jonghye Woo

Multimodal Magnetic Resonance (MR) Imaging plays a crucial role in disease diagnosis due to its ability to provide complementary information by analyzing a relationship between multimodal images on the same subject.

Brain Tumor Segmentation Translation +1

Learn From Orientation Prior for Radiograph Super-Resolution: Orientation Operator Transformer

no code implementations27 Dec 2023 Yongsong Huang, Tomo Miyazaki, Xiaofeng Liu, Kaiyuan Jiang, Zhengmi Tang, Shinichiro Omachi

Conclusions: In this study, we propose a novel framework called $O^{2}$former for radiological image super-resolution tasks, which improves the reconstruction model's performance by introducing an orientation operator and multi-scale feature fusion strategy.

Denoising Image Enhancement +1

Automated interpretation of congenital heart disease from multi-view echocardiograms

no code implementations30 Nov 2023 Jing Wang, Xiaofeng Liu, Fangyun Wang, Lin Zheng, Fengqiao Gao, Hanwen Zhang, Xin Zhang, Wanqing Xie, Binbin Wang

Our video-based model can diagnose with an accuracy of 93. 9\% (binary classification), and 92. 1\% (3-class classification) in a collected 2D video testing set, which does not need key-frame selection and view annotation in testing.

Binary Classification

Target-oriented Domain Adaptation for Infrared Image Super-Resolution

1 code implementation15 Nov 2023 Yongsong Huang, Tomo Miyazaki, Xiaofeng Liu, Yafei Dong, Shinichiro Omachi

DASRGAN operates on the synergy of two key components: 1) Texture-Oriented Adaptation (TOA) to refine texture details meticulously, and 2) Noise-Oriented Adaptation (NOA), dedicated to minimizing noise transfer.

Domain Adaptation Image Super-Resolution +1

Vicinal Feature Statistics Augmentation for Federated 3D Medical Volume Segmentation

no code implementations23 Oct 2023 Yongsong Huang, Wanqing Xie, Mingzhen Li, Mingmei Cheng, Jinzhou Wu, Weixiao Wang, Jane You, Xiaofeng Liu

However, the performance of FL can be constrained by the limited availability of labeled data in small institutes and the heterogeneous (i. e., non-i. i. d.)

Cardiac Segmentation Data Augmentation +2

Semi-Supervised End-To-End Contrastive Learning For Time Series Classification

no code implementations13 Oct 2023 Huili Cai, Xiang Zhang, Xiaofeng Liu

The unsupervised, supervised contrastive losses and classification loss are jointly used to optimize the encoder and classifier.

Classification Contrastive Learning +3

Bias and Fairness in Chatbots: An Overview

no code implementations16 Sep 2023 Jintang Xue, Yun-Cheng Wang, Chengwei Wei, Xiaofeng Liu, Jonghye Woo, C. -C. Jay Kuo

Thus, a comprehensive overview on bias and fairness in chatbot systems is given in this paper.

Chatbot Fairness

MRecGen: Multimodal Appropriate Reaction Generator

no code implementations5 Jul 2023 Jiaqi Xu, Cheng Luo, Weicheng Xie, Linlin Shen, Xiaofeng Liu, Lu Liu, Hatice Gunes, Siyang Song

Verbal and non-verbal human reaction generation is a challenging task, as different reactions could be appropriate for responding to the same behaviour.

Incremental Learning for Heterogeneous Structure Segmentation in Brain Tumor MRI

no code implementations30 May 2023 Xiaofeng Liu, Helen A. Shih, Fangxu Xing, Emiliano Santarnecchi, Georges El Fakhri, Jonghye Woo

Deep learning (DL) models for segmenting various anatomical structures have achieved great success via a static DL model that is trained in a single source domain.

Brain Tumor Segmentation Incremental Learning +3

Attentive Continuous Generative Self-training for Unsupervised Domain Adaptive Medical Image Translation

no code implementations23 May 2023 Xiaofeng Liu, Jerry L. Prince, Fangxu Xing, Jiachen Zhuo, Reese Timothy, Maureen Stone, Georges El Fakhri, Jonghye Woo

We evaluated our framework on two cross-scanner/center, inter-subject translation tasks, including tagged-to-cine magnetic resonance (MR) image translation and T1-weighted MR-to-fractional anisotropy translation.

Pseudo Label Pseudo Label Filtering +3

Advancing Unsupervised Low-light Image Enhancement: Noise Estimation, Illumination Interpolation, and Self-Regulation

1 code implementation17 May 2023 Xiaofeng Liu, Jiaxin Gao, Xin Fan, Risheng Liu

Contemporary Low-Light Image Enhancement (LLIE) techniques have made notable advancements in preserving image details and enhancing contrast, achieving commendable results on specific datasets.

Denoising Low-Light Image Enhancement +1

Motion-Scenario Decoupling for Rat-Aware Video Position Prediction: Strategy and Benchmark

no code implementations17 May 2023 Xiaofeng Liu, Jiaxin Gao, Yaohua Liu, Risheng Liu, Nenggan Zheng

Recently significant progress has been made in human action recognition and behavior prediction using deep learning techniques, leading to improved vision-based semantic understanding.

Action Recognition motion prediction +3

Posterior Estimation Using Deep Learning: A Simulation Study of Compartmental Modeling in Dynamic PET

no code implementations17 Mar 2023 Xiaofeng Liu, Thibault Marin, Tiss Amal, Jonghye Woo, Georges El Fakhri, Jinsong Ouyang

Purpose: This work aims at using deep learning to efficiently estimate posterior distributions of imaging parameters, which in turn can be used to derive the most probable parameters as well as their uncertainties.

Bayesian Inference Decoder

Synthesizing audio from tongue motion during speech using tagged MRI via transformer

no code implementations14 Feb 2023 Xiaofeng Liu, Fangxu Xing, Jerry L. Prince, Maureen Stone, Georges El Fakhri, Jonghye Woo

However, elucidating the relationship between these two sources of information is challenging, due in part to the disparity in data structure between spatiotemporal motion fields (i. e., 4D motion fields) and one-dimensional audio waveforms.

Decoder

Successive Subspace Learning for Cardiac Disease Classification with Two-phase Deformation Fields from Cine MRI

no code implementations21 Jan 2023 Xiaofeng Liu, Fangxu Xing, Hanna K. Gaggin, C. -C. Jay Kuo, Georges El Fakhri, Jonghye Woo

Cardiac cine magnetic resonance imaging (MRI) has been used to characterize cardiovascular diseases (CVD), often providing a noninvasive phenotyping tool.~While recently flourished deep learning based approaches using cine MRI yield accurate characterization results, the performance is often degraded by small training samples.

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

no code implementations16 Sep 2022 Xiaofeng Liu, Fangxu Xing, Georges El Fakhri, Jonghye Woo

Unsupervised domain adaptation (UDA) has been a vital protocol for migrating information learned from a labeled source domain to facilitate the implementation in an unlabeled heterogeneous target domain.

Brain Tumor Segmentation Image Segmentation +4

Tensor Decomposition based Personalized Federated Learning

no code implementations27 Aug 2022 Qing Wang, Jing Jin, Xiaofeng Liu, Huixuan Zong, Yunfeng Shao, Yinchuan Li

Federated learning (FL) is a new distributed machine learning framework that can achieve reliably collaborative training without collecting users' private data.

Model Optimization Personalized Federated Learning +1

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

no code implementations26 Aug 2022 Lingsheng Kong, Bo Hu, Xiongchang Liu, Jun Lu, Jane You, Xiaofeng Liu

Deep learning is usually data starved, and the unsupervised domain adaptation (UDA) is developed to introduce the knowledge in the labeled source domain to the unlabeled target domain.

Image Classification Pseudo Label +2

Unsupervised Domain Adaptation for Segmentation with Black-box Source Model

no code implementations16 Aug 2022 Xiaofeng Liu, Chaehwa Yoo, Fangxu Xing, C. -C. Jay Kuo, Georges El Fakhri, Jonghye Woo

Unsupervised domain adaptation (UDA) has been widely used to transfer knowledge from a labeled source domain to an unlabeled target domain to counter the difficulty of labeling in a new domain.

Knowledge Distillation Segmentation +1

Subtype-Aware Dynamic Unsupervised Domain Adaptation

no code implementations16 Aug 2022 Xiaofeng Liu, Fangxu Xing, Jia You, Jun Lu, C. -C. Jay Kuo, Georges El Fakhri, Jonghye Woo

In TPN, while the closeness of class centers between source and target domains is explicitly enforced in a latent space, the underlying fine-grained subtype structure and the cross-domain within-class compactness have not been fully investigated.

Unsupervised Domain Adaptation

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

no code implementations15 Aug 2022 Xiaofeng Liu, Chaehwa Yoo, Fangxu Xing, Hyejin Oh, Georges El Fakhri, Je-Won Kang, Jonghye Woo

Unsupervised domain adaptation (UDA) is proposed to counter this, by leveraging both labeled source domain data and unlabeled target domain data to carry out various tasks in the target domain.

Domain Generalization Out-of-Distribution Detection +3

AI Enlightens Wireless Communication: A Transformer Backbone for CSI Feedback

no code implementations16 Jun 2022 Han Xiao, Zhiqin Wang, Dexin Li, Wenqiang Tian, Xiaofeng Liu, Wendong Liu, Shi Jin, Jia Shen, Zhi Zhang, Ning Yang

This paper is based on the background of the 2nd Wireless Communication Artificial Intelligence (AI) Competition (WAIC) which is hosted by IMT-2020(5G) Promotion Group 5G+AIWork Group, where the framework of the eigenvector-based channel state information (CSI) feedback problem is firstly provided.

Data Augmentation

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

no code implementations5 Jun 2022 Xiaofeng Liu, Fangxu Xing, Nadya Shusharina, Ruth Lim, C-C Jay Kuo, Georges El Fakhri, Jonghye Woo

Unsupervised domain adaptation (UDA) has been vastly explored to alleviate domain shifts between source and target domains, by applying a well-performed model in an unlabeled target domain via supervision of a labeled source domain.

Image Segmentation MRI segmentation +4

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

no code implementations5 Jun 2022 Xiaofeng Liu, Fangxu Xing, Jerry L. Prince, Jiachen Zhuo, Maureen Stone, Georges El Fakhri, Jonghye Woo

Understanding the underlying relationship between tongue and oropharyngeal muscle deformation seen in tagged-MRI and intelligible speech plays an important role in advancing speech motor control theories and treatment of speech related-disorders.

Audio Synthesis Disentanglement

Sparse Federated Learning with Hierarchical Personalized Models

no code implementations25 Mar 2022 Xiaofeng Liu, Qing Wang, Yunfeng Shao, Yinchuan Li

To this end, we propose a personalized FL algorithm using a hierarchical proximal mapping based on the moreau envelop, named sparse federated learning with hierarchical personalized models (sFedHP), which significantly improves the global model performance facing diverse data.

Autonomous Vehicles Federated Learning

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

1 code implementation5 Mar 2022 Xiaofeng Liu, Yalan Song, Chaopeng Shen

We also found the surrogate architecture (whether with both velocity and water surface elevation or velocity only as outputs) does not show significant impact on inversion result.

Decoder Management

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

no code implementations25 Feb 2022 Xiaofeng Liu, Fangxu Xing, Jerry L. Prince, Maureen Stone, Georges El Fakhri, Jonghye Woo

Specifically, we propose a novel input-output image patches self-training scheme to achieve a disentanglement of underlying anatomical structures and imaging modalities.

Disentanglement Style Transfer

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

no code implementations22 Jan 2022 Kaiwen Tan, Weixian Huang, Xiaofeng Liu, Jinlong Hu, Shoubin Dong

By integrating these heterogeneous but complementary data, many multi-modal methods are proposed to study the complex mechanisms of cancers, and most of them achieve comparable or better results from previous single-modal methods.

Multi-Task Learning Survival Analysis

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

no code implementations18 Jan 2022 Xiaofeng Liu, Fangxu Xing, Thibault Marin, Georges El Fakhri, Jonghye Woo

Then, we apply a variational autoencoder network and optimize its evidence lower bound (ELBO) to efficiently approximate the distribution of the segmentation map, given an MR image.

MRI segmentation Segmentation +1

Self-semantic contour adaptation for cross modality brain tumor segmentation

no code implementations13 Jan 2022 Xiaofeng Liu, Fangxu Xing, Georges El Fakhri, Jonghye Woo

Unsupervised domain adaptation (UDA) between two significantly disparate domains to learn high-level semantic alignment is a crucial yet challenging task.~To this end, in this work, we propose exploiting low-level edge information to facilitate the adaptation as a precursor task, which has a small cross-domain gap, compared with semantic segmentation.~The precise contour then provides spatial information to guide the semantic adaptation.

Brain Tumor Segmentation Segmentation +2

Surrogate Model for Shallow Water Equations Solvers with Deep Learning

1 code implementation20 Dec 2021 Yalan Song, Chaopeng Shen, Xiaofeng Liu

The new method was evaluated and compared against existing methods based on convolutional neural networks (CNNs), which can only make image-to-image predictions on structured or regular meshes.

BACO: A Background Knowledge- and Content-Based Framework for Citing Sentence Generation

no code implementations ACL 2021 Yubin Ge, Ly Dinh, Xiaofeng Liu, Jinsong Su, Ziyao Lu, Ante Wang, Jana Diesner

In this paper, we focus on the problem of citing sentence generation, which entails generating a short text to capture the salient information in a cited paper and the connection between the citing and cited paper.

Sentence Text Generation

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

no code implementations ICCV 2021 Xiaofeng Liu, Zhenhua Guo, Site Li, Fangxu Xing, Jane You, C. -C. Jay Kuo, Georges El Fakhri, Jonghye Woo

In this work, we propose an adversarial unsupervised domain adaptation (UDA) approach with the inherent conditional and label shifts, in which we aim to align the distributions w. r. t.

Unsupervised Domain Adaptation

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

no code implementations22 Jul 2021 Wanqing Xie, Lizhong Liang, Yao Lu, Hui Luo, Xiaofeng Liu

The superior performance of our system shows the validity of combining facial video recording with the SDS score for more accurate self-diagnose.

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

no code implementations22 Jul 2021 Xiaofeng Liu, Fangxu Xing, Hanna K. Gaggin, Weichung Wang, C. -C. Jay Kuo, Georges El Fakhri, Jonghye Woo

Assessment of cardiovascular disease (CVD) with cine magnetic resonance imaging (MRI) has been used to non-invasively evaluate detailed cardiac structure and function.

Dimensionality Reduction feature selection +2

Domain Generalization under Conditional and Label Shifts via Variational Bayesian Inference

no code implementations22 Jul 2021 Xiaofeng Liu, Bo Hu, Linghao Jin, Xu Han, Fangxu Xing, Jinsong Ouyang, Jun Lu, Georges El Fakhri, Jonghye Woo

In this work, we propose a domain generalization (DG) approach to learn on several labeled source domains and transfer knowledge to a target domain that is inaccessible in training.

Bayesian Inference Domain Generalization

Sparse Personalized Federated Learning

no code implementations12 Jul 2021 Xiaofeng Liu, Yinchuan Li, Qing Wang, Xu Zhang, Yunfeng Shao, Yanhui Geng

By incorporating an approximated L1-norm and the correlation between client models and global model into standard FL loss function, the performance on statistical diversity data is improved and the communicational and computational loads required in the network are reduced compared with non-sparse FL.

Personalized Federated Learning

Structured Directional Pruning via Perturbation Orthogonal Projection

no code implementations12 Jul 2021 Yinchuan Li, Xiaofeng Liu, Yunfeng Shao, Qing Wang, Yanhui Geng

Structured pruning is an effective compression technique to reduce the computation of neural networks, which is usually achieved by adding perturbations to reduce network parameters at the cost of slightly increasing training loss.

Interpreting Depression From Question-wise Long-term Video Recording of SDS Evaluation

no code implementations25 Jun 2021 Wanqing Xie, Lizhong Liang, Yao Lu, Chen Wang, Jihong Shen, Hui Luo, Xiaofeng Liu

To automatically interpret depression from the SDS evaluation and the paired video, we propose an end-to-end hierarchical framework for the long-term variable-length video, which is also conditioned on the questionnaire results and the answering time.

Depression Detection

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

no code implementations23 Jun 2021 Xiaofeng Liu, Fangxu Xing, Chao Yang, Georges El Fakhri, Jonghye Woo

To alleviate this, in this work, we target source free UDA for segmentation, and propose to adapt an ``off-the-shelf" segmentation model pre-trained in the source domain to the target domain, with an adaptive batch-wise normalization statistics adaptation framework.

Image Segmentation Medical Image Segmentation +3

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

no code implementations23 Jun 2021 Xiaofeng Liu, Fangxu Xing, Maureen Stone, Jiachen Zhuo, Reese Timothy, Jerry L. Prince, Georges El Fakhri, Jonghye Woo

Self-training based unsupervised domain adaptation (UDA) has shown great potential to address the problem of domain shift, when applying a trained deep learning model in a source domain to unlabeled target domains.

Image Generation Pseudo Label +3

AI Enlightens Wireless Communication: Analyses, Solutions and Opportunities on CSI Feedback

no code implementations12 Jun 2021 Han Xiao, Zhiqin Wang, Wenqiang Tian, Xiaofeng Liu, Wendong Liu, Shi Jin, Jia Shen, Zhi Zhang, Ning Yang

In this paper, we give a systematic description of the 1st Wireless Communication Artificial Intelligence (AI) Competition (WAIC) which is hosted by IMT-2020(5G) Promotion Group 5G+AI Work Group.

Quantization

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

no code implementations17 Jan 2021 Xiaofeng Liu, Fangxu Xing, Chao Yang, C. -C. Jay Kuo, Georges ElFakhri, Jonghye Woo

Deformable registration of magnetic resonance images between patients with brain tumors and healthy subjects has been an important tool to specify tumor geometry through location alignment and facilitate pathological analysis.

Brain Tumor Segmentation Image Inpainting +3

A Unified Conditional Disentanglement Framework for Multimodal Brain MR Image Translation

no code implementations14 Jan 2021 Xiaofeng Liu, Fangxu Xing, Georges El Fakhri, Jonghye Woo

Our framework hinges on a cycle-constrained conditional adversarial training approach, where it can extract a modality-invariant anatomical feature with a modality-agnostic encoder and generate a target modality with a conditioned decoder.

Decoder Disentanglement +2

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

no code implementations13 Jan 2021 Xiaofeng Liu, Fangxu Xing, Chao Yang, C. -C. Jay Kuo, Suma Babu, Georges El Fakhri, Thomas Jenkins, Jonghye Woo

Deep learning has great potential for accurate detection and classification of diseases with medical imaging data, but the performance is often limited by the number of training datasets and memory requirements.

Classification Dimensionality Reduction +1

Subtype-aware Unsupervised Domain Adaptation for Medical Diagnosis

no code implementations1 Jan 2021 Xiaofeng Liu, Xiongchang Liu, Bo Hu, Wenxuan Ji, Fangxu Xing, Jun Lu, Jane You, C. -C. Jay Kuo, Georges El Fakhri, Jonghye Woo

Recent advances in unsupervised domain adaptation (UDA) show that transferable prototypical learning presents a powerful means for class conditional alignment, which encourages the closeness of cross-domain class centroids.

Medical Diagnosis Unsupervised Domain Adaptation

Energy-constrained Self-training for Unsupervised Domain Adaptation

no code implementations1 Jan 2021 Xiaofeng Liu, Bo Hu, Xiongchang Liu, Jun Lu, Jane You, Lingsheng Kong

Unsupervised domain adaptation (UDA) aims to transfer the knowledge on a labeled source domain distribution to perform well on an unlabeled target domain.

Image Classification Semantic Segmentation +1

Identity-aware Facial Expression Recognition in Compressed Video

no code implementations1 Jan 2021 Xiaofeng Liu, Linghao Jin, Xu Han, Jun Lu, Jane You, Lingsheng Kong

In the up to two orders of magnitude compressed domain, we can explicitly infer the expression from the residual frames and possible to extract identity factors from the I frame with a pre-trained face recognition network.

Face Recognition Facial Expression Recognition +1

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

no code implementations21 Oct 2020 Xiaofeng Liu, Yuzhuo Han, Song Bai, Yi Ge, Tianxing Wang, Xu Han, Site Li, Jane You, Ju Lu

However, the cross entropy loss can not take the different importance of each class in an self-driving system into account.

Segmentation Self-Driving Cars +1

Mutual Information Regularized Identity-aware Facial ExpressionRecognition in Compressed Video

no code implementations20 Oct 2020 Xiaofeng Liu, Linghao Jin, Xu Han, Jane You

In the up to two orders of magnitude compressed domain, we can explicitly infer the expression from the residual frames and possibly extract identity factors from the I frame with a pre-trained face recognition network.

Face Recognition Facial Expression Recognition +1

Disentanglement for Discriminative Visual Recognition

no code implementations14 Jun 2020 Xiaofeng Liu

This chapter systematically summarize the detrimental factors as task-relevant/irrelevant semantic variations and unspecified latent variation.

Disentanglement Face Recognition +3

Severity-Aware Semantic Segmentation With Reinforced Wasserstein Training

no code implementations CVPR 2020 Xiaofeng Liu, Wenxuan Ji, Jane You, Georges El Fakhri, Jonghye Woo

In addition, our method can adaptively learn the ground metric in a high-fidelity simulator, following a reinforcement alternative optimization scheme.

Autonomous Vehicles Semantic Segmentation

TPPO: A Novel Trajectory Predictor with Pseudo Oracle

no code implementations4 Feb 2020 Biao Yang, Caizhen He, Pin Wang, Ching-Yao Chan, Xiaofeng Liu, Yang Chen

A latent variable predictor is proposed to estimate latent variable distributions from observed and ground-truth trajectories.

Autonomous Driving Human-Object Interaction Detection

Towards Disentangled Representations for Human Retargeting by Multi-view Learning

no code implementations12 Dec 2019 Chao Yang, Xiaofeng Liu, Qingming Tang, C. -C. Jay Kuo

We study the problem of learning disentangled representations for data across multiple domains and its applications in human retargeting.

MULTI-VIEW LEARNING

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

no code implementations18 Nov 2019 Tong Che, Xiaofeng Liu, Site Li, Yubin Ge, Ruixiang Zhang, Caiming Xiong, Yoshua Bengio

We test the verifier network on out-of-distribution detection and adversarial example detection problems, as well as anomaly detection problems in structured prediction tasks such as image caption generation.

Anomaly Detection Autonomous Driving +4

Conservative Wasserstein Training for Pose Estimation

no code implementations ICCV 2019 Xiaofeng Liu, Yang Zou, Tong Che, Peng Ding, Ping Jia, Jane You, Kumar B. V. K

We propose to incorporate inter-class correlations in a Wasserstein training framework by pre-defining ($i. e.,$ using arc length of a circle) or adaptively learning the ground metric.

Pose Estimation

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

no code implementations5 Aug 2019 Xiaofeng Liu, Zhenhua Guo, Jane You, B. V. K. Vijaya Kumar

The importance of each image is usually considered either equal or based on a quality assessment of that image independent of other images and/or videos in that image set.

Face Recognition Face Verification +1

PNS: Population-Guided Novelty Search for Reinforcement Learning in Hard Exploration Environments

no code implementations26 Nov 2018 Qihao Liu, Yujia Wang, Xiaofeng Liu

To balance exploration and exploitation, the Novelty Search (NS) is employed in every chief agent to encourage policies with high novelty while maximizing per-episode performance.

Continuous Control reinforcement-learning +1

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

no code implementations23 Mar 2018 Chao Yang, Yuhang Song, Xiaofeng Liu, Qingming Tang, C. -C. Jay Kuo

We present a new approach to address the difficulty of training a very deep generative model to synthesize high-quality photo-realistic inpainting.

Facial Inpainting Image Harmonization

Contextual-based Image Inpainting: Infer, Match, and Translate

no code implementations ECCV 2018 Yuhang Song, Chao Yang, Zhe Lin, Xiaofeng Liu, Qin Huang, Hao Li, C. -C. Jay Kuo

We study the task of image inpainting, which is to fill in the missing region of an incomplete image with plausible contents.

Image Inpainting Translation

Fast Online Clustering with Randomized Skeleton Sets

no code implementations10 Jun 2015 Krzysztof Choromanski, Sanjiv Kumar, Xiaofeng Liu

To achieve fast clustering, we propose to represent each cluster by a skeleton set which is updated continuously as new data is seen.

Clustering Nonparametric Clustering +1

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