Search Results for author: Xi Peng

Found 56 papers, 18 papers with code

Deep Learning for Spatiotemporal Modeling of Urbanization

no code implementations17 Dec 2021 Tang Li, Jing Gao, Xi Peng

Here we explore the capacity of deep spatial learning for the predictive modeling of urbanization.

Learning with Noisy Correspondence for Cross-modal Matching

no code implementations NeurIPS 2021 Zhenyu Huang, guocheng niu, Xiao Liu, Wenbiao Ding, Xinyan Xiao, Hua Wu, Xi Peng

Based on this observation, we reveal and study a latent and challenging direction in cross-modal matching, named noisy correspondence, which could be regarded as a new paradigm of noisy labels.

Cross-Modal Retrieval Text Matching

Calibrating Probabilistic Embeddings for Cross-Modal Retrieval

no code implementations29 Sep 2021 Fengchun Qiao, Xi Peng

The key idea is to estimate the density ratio between the distributions of the two modalities, and use it to calibrate the similarity measurement in the embedding space.

Cross-Modal Retrieval

Understanding the factors driving the opioid epidemic using machine learning

no code implementations16 Aug 2021 Sachin Gavali, Chuming Chen, Julie Cowart, Xi Peng, Shanshan Ding, Cathy Wu, Tammy Anderson

Furthermore, we discovered that, as the epidemic has shifted from legal (i. e., prescription opioids) to illegal (e. g., heroin and fentanyl) drugs in recent years, the correlation of environment, crime and health related variables with the opioid risk has increased significantly while the correlation of economic and socio-demographic variables has decreased.

Out-of-domain Generalization from a Single Source: A Uncertainty Quantification Approach

no code implementations5 Aug 2021 Xi Peng, Fengchun Qiao, Long Zhao

To the best of our knowledge, this is the first work to (1) quantify the generalization uncertainty from a single source and (2) leverage it to guide both feature and label augmentation for robust generalization.

Domain Generalization Image Classification +4

Accelerated MRI Reconstruction with Separable and Enhanced Low-Rank Hankel Regularization

no code implementations24 Jul 2021 Xinlin Zhang, Hengfa Lu, Di Guo, Zongying Lai, Huihui Ye, Xi Peng, Bo Zhao, Xiaobo Qu

The combination of the sparse sampling and the low-rank structured matrix reconstruction has shown promising performance, enabling a significant reduction of the magnetic resonance imaging data acquisition time.

MRI Reconstruction

Unsupervised Neural Rendering for Image Hazing

no code implementations14 Jul 2021 Boyun Li, Yijie Lin, Xiao Liu, Peng Hu, Jiancheng Lv, Xi Peng

To generate plausible haze, we study two less-touched but challenging problems in hazy image rendering, namely, i) how to estimate the transmission map from a single image without auxiliary information, and ii) how to adaptively learn the airlight from exemplars, i. e., unpaired real hazy images.

Image Dehazing Neural Rendering

Dizygotic Conditional Variational AutoEncoder for Multi-Modal and Partial Modality Absent Few-Shot Learning

no code implementations28 Jun 2021 Yi Zhang, Sheng Huang, Xi Peng, Dan Yang

DCVAE conducts feature synthesis via pairing two Conditional Variational AutoEncoders (CVAEs) with the same seed but different modality conditions in a dizygotic symbiosis manner.

Data Augmentation Few-Shot Learning

COMPLETER: Incomplete Multi-view Clustering via Contrastive Prediction

1 code implementation CVPR 2021 Yijie Lin, Yuanbiao Gou, Zitao Liu, Boyun Li, Jiancheng Lv, Xi Peng

In this paper, we study two challenging problems in incomplete multi-view clustering analysis, namely, i) how to learn an informative and consistent representation among different views without the help of labels and ii) how to recover the missing views from data.

Contrastive Learning Incomplete multi-view clustering +1

Uncertainty-guided Model Generalization to Unseen Domains

no code implementations CVPR 2021 Fengchun Qiao, Xi Peng

To the best of our knowledge, this is the first work to (1) access the generalization uncertainty from a single source and (2) leverage it to guide both input and label augmentation for robust generalization.

Domain Generalization Image Classification +4

SMIL: Multimodal Learning with Severely Missing Modality

1 code implementation9 Mar 2021 Mengmeng Ma, Jian Ren, Long Zhao, Sergey Tulyakov, Cathy Wu, Xi Peng

A common assumption in multimodal learning is the completeness of training data, i. e., full modalities are available in all training examples.


Uncertain Out-of-Domain Generalization

no code implementations1 Jan 2021 Fengchun Qiao, Xi Peng

To the best of our knowledge, this is the first work to (1) access the generalization uncertainty from a single source and (2) leverage it to guide both input and label augmentation for robust generalization.

Domain Generalization Image Classification +3

Learning View-Disentangled Human Pose Representation by Contrastive Cross-View Mutual Information Maximization

1 code implementation CVPR 2021 Long Zhao, Yuxiao Wang, Jiaping Zhao, Liangzhe Yuan, Jennifer J. Sun, Florian Schroff, Hartwig Adam, Xi Peng, Dimitris Metaxas, Ting Liu

To evaluate the power of the learned representations, in addition to the conventional fully-supervised action recognition settings, we introduce a novel task called single-shot cross-view action recognition.

Action Recognition Contrastive Learning +1

CLEARER: Multi-Scale Neural Architecture Search for Image Restoration

1 code implementation NeurIPS 2020 Yuanbiao Gou, Boyun Li, Zitao Liu, Songfan Yang, Xi Peng

Different from the existing labor-intensive handcrafted architecture design paradigms, we present a novel method, termed as multi-sCaLe nEural ARchitecture sEarch for image Restoration (CLEARER), which is a specifically designed neural architecture search (NAS) for image restoration.

Image Denoising Image Restoration +2

Partially View-aligned Clustering

no code implementations NeurIPS 2020 Zhenyu Huang, Peng Hu, Joey Tianyi Zhou, Jiancheng Lv, Xi Peng

To solve this practical and challenging problem, we propose a novel multi-view clustering method termed partially view-aligned clustering (PVC).

Contrastive Clustering

1 code implementation21 Sep 2020 Yunfan Li, Peng Hu, Zitao Liu, Dezhong Peng, Joey Tianyi Zhou, Xi Peng

In this paper, we propose a one-stage online clustering method called Contrastive Clustering (CC) which explicitly performs the instance- and cluster-level contrastive learning.

Ranked #4 on Image Clustering on STL-10 (using extra training data)

Contrastive Learning Online Clustering

Structured Graph Learning for Clustering and Semi-supervised Classification

no code implementations31 Aug 2020 Zhao Kang, Chong Peng, Qiang Cheng, Xinwang Liu, Xi Peng, Zenglin Xu, Ling Tian

Furthermore, most existing graph-based methods conduct clustering and semi-supervised classification on the graph learned from the original data matrix, which doesn't have explicit cluster structure, thus they might not achieve the optimal performance.

General Classification Graph Learning

You Only Look Yourself: Unsupervised and Untrained Single Image Dehazing Neural Network

1 code implementation30 Jun 2020 Boyun Li, Yuanbiao Gou, Shuhang Gu, Jerry Zitao Liu, Joey Tianyi Zhou, Xi Peng

In this paper, we study two challenging and less-touched problems in single image dehazing, namely, how to make deep learning achieve image dehazing without training on the ground-truth clean image (unsupervised) and a image collection (untrained).

Image Dehazing Single Image Dehazing

Heterogeneous Representation Learning: A Review

no code implementations28 Apr 2020 Joey Tianyi Zhou, Xi Peng, Yew-Soon Ong

The real-world data usually exhibits heterogeneous properties such as modalities, views, or resources, which brings some unique challenges wherein the key is Heterogeneous Representation Learning (HRL) termed in this paper.

Multi-Task Learning MULTI-VIEW LEARNING +1

Knowledge as Priors: Cross-Modal Knowledge Generalization for Datasets without Superior Knowledge

no code implementations CVPR 2020 Long Zhao, Xi Peng, Yuxiao Chen, Mubbasir Kapadia, Dimitris N. Metaxas

Our key idea is to generalize the distilled cross-modal knowledge learned from a Source dataset, which contains paired examples from both modalities, to the Target dataset by modeling knowledge as priors on parameters of the Student.

3D Hand Pose Estimation Knowledge Distillation

Learning to Learn Single Domain Generalization

1 code implementation CVPR 2020 Fengchun Qiao, Long Zhao, Xi Peng

We are concerned with a worst-case scenario in model generalization, in the sense that a model aims to perform well on many unseen domains while there is only one single domain available for training.

Domain Generalization Meta-Learning

Improving Distant Supervised Relation Extraction by Dynamic Neural Network

no code implementations15 Nov 2019 Yanjie Gou, Yinjie Lei, Lingqiao Liu, Pingping Zhang, Xi Peng

To account for this style shift, the model should adjust its parameters in accordance with entity types.

Relation Extraction

Automatic Health Problem Detection from Gait Videos Using Deep Neural Networks

1 code implementation4 Jun 2019 Rahil Mehrizi, Xi Peng, Shaoting Zhang, Ruisong Liao, Kang Li

This study presents a starting point toward a powerful tool for automatic classification of gait disorders and can be used as a basis for future applications of Deep Learning in clinical gait analysis.

Feature Engineering General Classification +2

Semantic-Guided Multi-Attention Localization for Zero-Shot Learning

no code implementations NeurIPS 2019 Yizhe Zhu, Jianwen Xie, Zhiqiang Tang, Xi Peng, Ahmed Elgammal

Zero-shot learning extends the conventional object classification to the unseen class recognition by introducing semantic representations of classes.

Zero-Shot Learning

k-meansNet: When k-means Meets Differentiable Programming

no code implementations22 Aug 2018 Xi Peng, Ivor W. Tsang, Joey Tianyi Zhou, Hongyuan Zhu

From the view of neural networks, the proposed \textit{k}-meansNet is with explicit interpretability in neural processing.

Quantized Densely Connected U-Nets for Efficient Landmark Localization

1 code implementation ECCV 2018 Zhiqiang Tang, Xi Peng, Shijie Geng, Lingfei Wu, Shaoting Zhang, Dimitris Metaxas

Finally, to reduce the memory consumption and high precision operations both in training and testing, we further quantize weights, inputs, and gradients of our localization network to low bit-width numbers.

Face Alignment Pose Estimation

Learning to Forecast and Refine Residual Motion for Image-to-Video Generation

1 code implementation ECCV 2018 Long Zhao, Xi Peng, Yu Tian, Mubbasir Kapadia, Dimitris Metaxas

We consider the problem of image-to-video translation, where an input image is translated into an output video containing motions of a single object.

Human Pose Forecasting Translation +1

CR-GAN: Learning Complete Representations for Multi-view Generation

1 code implementation28 Jun 2018 Yu Tian, Xi Peng, Long Zhao, Shaoting Zhang, Dimitris N. Metaxas

Generating multi-view images from a single-view input is an essential yet challenging problem.

Self-Supervised Learning

Toward Marker-free 3D Pose Estimation in Lifting: A Deep Multi-view Solution

no code implementations6 Feb 2018 Rahil Mehrizi, Xi Peng, Zhiqiang Tang, Xu Xu, Dimitris Metaxas, Kang Li

The results are also compared with state-of-the-art methods on HumanEva-I dataset, which demonstrates the superior performance of our approach.

3D Pose Estimation

RED-Net: A Recurrent Encoder-Decoder Network for Video-based Face Alignment

no code implementations17 Jan 2018 Xi Peng, Rogerio S. Feris, Xiaoyu Wang, Dimitris N. Metaxas

We propose a novel method for real-time face alignment in videos based on a recurrent encoder-decoder network model.

Face Alignment

Deep Sparse Subspace Clustering

no code implementations25 Sep 2017 Xi Peng, Jiashi Feng, Shijie Xiao, Jiwen Lu, Zhang Yi, Shuicheng Yan

In this paper, we present a deep extension of Sparse Subspace Clustering, termed Deep Sparse Subspace Clustering (DSSC).

Cartoonish sketch-based face editing in videos using identity deformation transfer

no code implementations25 Mar 2017 Long Zhao, Fangda Han, Xi Peng, Xun Zhang, Mubbasir Kapadia, Vladimir Pavlovic, Dimitris N. Metaxas

We first recover the facial identity and expressions from the video by fitting a face morphable model for each frame.

Face Model

Reconstruction-Based Disentanglement for Pose-invariant Face Recognition

no code implementations ICCV 2017 Xi Peng, Xiang Yu, Kihyuk Sohn, Dimitris Metaxas, Manmohan Chandraker

Finally, we propose a new feature reconstruction metric learning to explicitly disentangle identity and pose, by demanding alignment between the feature reconstructions through various combinations of identity and pose features, which is obtained from two images of the same subject.

Face Recognition Metric Learning +1

Track Facial Points in Unconstrained Videos

no code implementations9 Sep 2016 Xi Peng, Qiong Hu, Junzhou Huang, Dimitris N. Metaxas

Our approach takes advantage of part-based representation and cascade regression for robust and efficient alignment on each frame.

Incremental Learning

A Recurrent Encoder-Decoder Network for Sequential Face Alignment

no code implementations19 Aug 2016 Xi Peng, Rogerio S. Feris, Xiaoyu Wang, Dimitris N. Metaxas

We propose a novel recurrent encoder-decoder network model for real-time video-based face alignment.

Face Alignment

PIEFA: Personalized Incremental and Ensemble Face Alignment

no code implementations ICCV 2015 Xi Peng, Shaoting Zhang, Yu Yang, Dimitris N. Metaxas

Face alignment, especially on real-time or large-scale sequential images, is a challenging task with broad applications.

Face Alignment Incremental Learning

Connections Between Nuclear Norm and Frobenius Norm Based Representations

no code implementations26 Feb 2015 Xi Peng, Can-Yi Lu, Zhang Yi, Huajin Tang

A lot of works have shown that frobenius-norm based representation (FNR) is competitive to sparse representation and nuclear-norm based representation (NNR) in numerous tasks such as subspace clustering.

Automatic Subspace Learning via Principal Coefficients Embedding

no code implementations17 Nov 2014 Xi Peng, Jiwen Lu, Zhang Yi, Rui Yan

In this paper, we address two challenging problems in unsupervised subspace learning: 1) how to automatically identify the feature dimension of the learned subspace (i. e., automatic subspace learning), and 2) how to learn the underlying subspace in the presence of Gaussian noise (i. e., robust subspace learning).

Fast Low-rank Representation based Spatial Pyramid Matching for Image Classification

no code implementations22 Sep 2014 Xi Peng, Rui Yan, Bo Zhao, Huajin Tang, Zhang Yi

Although the methods achieve a higher recognition rate than the traditional SPM, they consume more time to encode the local descriptors extracted from the image.

General Classification Image Classification +1

A Unified Framework for Representation-based Subspace Clustering of Out-of-sample and Large-scale Data

no code implementations25 Sep 2013 Xi Peng, Huajin Tang, Lei Zhang, Zhang Yi, Shijie Xiao

In this paper, we propose a unified framework which makes representation-based subspace clustering algorithms feasible to cluster both out-of-sample and large-scale data.

Scalable Sparse Subspace Clustering

no code implementations CVPR 2013 Xi Peng, Lei Zhang, Zhang Yi

To address the problems, this paper proposes out-of-sample extension of SSC, named as Scalable Sparse Subspace Clustering (SSSC), which makes SSC feasible to cluster large scale data sets.

Motion Segmentation Online Clustering

Locally linear representation for image clustering

no code implementations24 Apr 2013 Liangli Zhen, Zhang Yi, Xi Peng, Dezhong Peng

There are two popular schemes to construct a similarity graph, i. e., pairwise distance based scheme and linear representation based scheme.

Image Clustering

Learning Locality-Constrained Collaborative Representation for Face Recognition

no code implementations4 Oct 2012 Xi Peng, Lei Zhang, Zhang Yi, Kok Kiong Tan

The model of low-dimensional manifold and sparse representation are two well-known concise models that suggest each data can be described by a few characteristics.

Dimensionality Reduction Face Recognition

Constructing the L2-Graph for Robust Subspace Learning and Subspace Clustering

no code implementations5 Sep 2012 Xi Peng, Zhiding Yu, Huajin Tang, Zhang Yi

Under the framework of graph-based learning, the key to robust subspace clustering and subspace learning is to obtain a good similarity graph that eliminates the effects of errors and retains only connections between the data points from the same subspace (i. e., intra-subspace data points).

Image Clustering Motion Segmentation

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