Search Results for author: Yue Wu

Found 81 papers, 24 papers with code

Unsupervised cross domain learning with applications to 7 layer segmentation of OCTs

no code implementations23 Nov 2021 Yue Wu, Abraham Olvera Barrios, Ryan Yanagihara, Irene Leung, Marian Blazes, Adnan Tufail, Aaron Lee

Unsupervised cross domain adaptation for OCT 7 layer segmentation and other medical applications where labeled training data is only available in a source domain and unavailable in the target domain.

Domain Adaptation

ADDS: Adaptive Differentiable Sampling for Robust Multi-Party Learning

no code implementations29 Oct 2021 Maoguo Gong, Yuan Gao, Yue Wu, A. K. Qin

Inspired by the idea of dropout in neural networks, we introduce a network sampling strategy in the multi-party setting, which distributes different subnets of the central model to clients for updating, and the differentiable sampling rates allow each client to extract optimal local architecture from the supernet according to its private data distribution.

Adaptive Sampling for Heterogeneous Rank Aggregation from Noisy Pairwise Comparisons

no code implementations8 Oct 2021 Yue Wu, Tao Jin, Hao Lou, Pan Xu, Farzad Farnoud, Quanquan Gu

In heterogeneous rank aggregation problems, users often exhibit various accuracy levels when comparing pairs of items.

Causal Triple Attention Time Series Forecasting

no code implementations29 Sep 2021 Zhixuan Chu, Tan Yan, Yue Wu, Yi Xu, Cheng Zhang, Yulin kang

Time series forecasting has historically been a key area of academic research and industrial applications.

Causal Inference Time Series +1

Embedding Novel Views in a Single JPEG Image

no code implementations ICCV 2021 Yue Wu, Guotao Meng, Qifeng Chen

We propose a novel approach for embedding novel views in a single JPEG image while preserving the perceptual fidelity of the modified JPEG image and the restored novel views.

Novel View Synthesis

Towards Photorealistic Colorization by Imagination

no code implementations20 Aug 2021 Chenyang Lei, Yue Wu, Qifeng Chen

We present a novel approach to automatic image colorization by imitating the imagination process of human experts.

Colorization Image Generation

Demonstration-Guided Reinforcement Learning with Learned Skills

no code implementations ICLR Workshop SSL-RL 2021 Karl Pertsch, Youngwoon Lee, Yue Wu, Joseph J. Lim

Prior approaches for demonstration-guided RL treat every new task as an independent learning problem and attempt to follow the provided demonstrations step-by-step, akin to a human trying to imitate a completely unseen behavior by following the demonstrator's exact muscle movements.

Video Super-Resolution with Long-Term Self-Exemplars

no code implementations24 Jun 2021 Guotao Meng, Yue Wu, Sijin Li, Qifeng Chen

Existing video super-resolution methods often utilize a few neighboring frames to generate a higher-resolution image for each frame.

Video Super-Resolution

SKFAC: Training Neural Networks With Faster Kronecker-Factored Approximate Curvature

1 code implementation CVPR 2021 Zedong Tang, Fenlong Jiang, Maoguo Gong, Hao Li, Yue Wu, Fan Yu, Zidong Wang, Min Wang

For the fully connected layers, by utilizing the low-rank property of Kronecker factors of Fisher information matrix, our method only requires inverting a small matrix to approximate the curvature with desirable accuracy.

Dimensionality Reduction

Feature Flow Regularization: Improving Structured Sparsity in Deep Neural Networks

no code implementations5 Jun 2021 Yue Wu, Yuan Lan, Luchan Zhang, Yang Xiang

Pruning is a model compression method that removes redundant parameters in deep neural networks (DNNs) while maintaining accuracy.

Model Compression

SKFAC:Training Neural Networks with Faster Kronecker-Factored Approximate Curvature

1 code implementation Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2021 Zedong Tang, Fenlong Jiang, Maoguo Gong, Hao Li, Yue Wu, Fan Yu, Zidong Wang, Min Wang

For the fully connected layers, by utilizing the low-rank property of Kronecker factors of Fisher information matrix, our method only requires inverting a small matrix to approximate the curvature with desirable accuracy.

Dimensionality Reduction

CRT-Net: A Generalized and Scalable Framework for the Computer-Aided Diagnosis of Electrocardiogram Signals

no code implementations28 May 2021 Jingyi Liu, Zhongyu Li, Xiayue Fan, Jintao Yan, Bolin Li, Xuemeng Hu, Qing Xia, Yue Wu

Subsequently, a novel deep neural network, namely CRT-Net, is designed for the fine-grained and comprehensive representation and recognition of 1-D ECG signals.

LineCounter: Learning Handwritten Text Line Segmentation by Counting

1 code implementation24 May 2021 Deng Li, Yue Wu, Yicong Zhou

In this paper, we propose a novel Line Counting formulation for HTLS -- that involves counting the number of text lines from the top at every pixel location.

Object Detection Semantic Segmentation

Uncertainty Weighted Actor-Critic for Offline Reinforcement Learning

2 code implementations17 May 2021 Yue Wu, Shuangfei Zhai, Nitish Srivastava, Joshua Susskind, Jian Zhang, Ruslan Salakhutdinov, Hanlin Goh

Offline Reinforcement Learning promises to learn effective policies from previously-collected, static datasets without the need for exploration.

Offline RL Q-Learning

Maximizing Mutual Information Across Feature and Topology Views for Learning Graph Representations

no code implementations14 May 2021 Xiaolong Fan, Maoguo Gong, Yue Wu, Hao Li

Specifically, we first utilize a multi-view representation learning module to better capture both local and global information content across feature and topology views on graphs.

Graph Representation Learning

SauvolaNet: Learning Adaptive Sauvola Network for Degraded Document Binarization

1 code implementation12 May 2021 Deng Li, Yue Wu, Yicong Zhou

The AST module further consolidates the outputs from MWS and PWA and predicts the final adaptive threshold for each pixel location.

Binarization Document Binarization

Spatially Self-Paced Convolutional Networks for Change Detection in Heterogeneous Images

no code implementations IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2021 Hao Li, Maoguo Gong, Mingyang Zhang, Yue Wu

Change detection in heterogeneous remote sensing images is a challenging problem because it is hard to make a direct comparison in the original observation spaces, and most methods rely on a set of manually labeled samples.

Style-Aware Normalized Loss for Improving Arbitrary Style Transfer

no code implementations CVPR 2021 Jiaxin Cheng, Ayush Jaiswal, Yue Wu, Pradeep Natarajan, Prem Natarajan

Neural Style Transfer (NST) has quickly evolved from single-style to infinite-style models, also known as Arbitrary Style Transfer (AST).

Style Transfer

Modelling Paralinguistic Properties in Conversational Speech to Detect Bipolar Disorder and Borderline Personality Disorder

no code implementations18 Feb 2021 Bo wang, Yue Wu, Nemanja Vaci, Maria Liakata, Terry Lyons, Kate E A Saunders

Bipolar disorder (BD) and borderline personality disorder (BPD) are two chronic mental health conditions that clinicians find challenging to distinguish based on clinical interviews, due to their overlapping symptoms.

Nearly Minimax Optimal Regret for Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation

no code implementations15 Feb 2021 Yue Wu, Dongruo Zhou, Quanquan Gu

We study reinforcement learning in an infinite-horizon average-reward setting with linear function approximation, where the transition probability function of the underlying Markov Decision Process (MDP) admits a linear form over a feature mapping of the current state, action, and next state.

Uncertainty Weighted Offline Reinforcement Learning

no code implementations1 Jan 2021 Yue Wu, Shuangfei Zhai, Nitish Srivastava, Joshua M. Susskind, Jian Zhang, Ruslan Salakhutdinov, Hanlin Goh

Offline Reinforcement Learning promises to learn effective policies from previously-collected, static datasets without the need for exploration.

Offline RL Q-Learning

VideoFlow: A Framework for Building Visual Analysis Pipelines

no code implementations1 Jan 2021 Yue Wu, Jianqiang Huang, Jiangjie Zhen, Guokun Wang, Chen Shen, Chang Zhou, Xian-Sheng Hua

The past years have witnessed an explosion of deep learning frameworks like PyTorch and TensorFlow since the success of deep neural networks.

A Finite-Time Analysis of Two Time-Scale Actor-Critic Methods

no code implementations NeurIPS 2020 Yue Wu, Weitong Zhang, Pan Xu, Quanquan Gu

In this work, we provide a non-asymptotic analysis for two time-scale actor-critic methods under non-i. i. d.

Class-agnostic Object Detection

no code implementations28 Nov 2020 Ayush Jaiswal, Yue Wu, Pradeep Natarajan, Premkumar Natarajan

Finally, we propose (1) baseline methods and (2) a new adversarial learning framework for class-agnostic detection that forces the model to exclude class-specific information from features used for predictions.

Class-agnostic Object Detection Visual Grounding

Claw U-Net: A Unet-based Network with Deep Feature Concatenation for Scleral Blood Vessel Segmentation

no code implementations20 Oct 2020 Chang Yao, Jingyu Tang, Menghan Hu, Yue Wu, Wenyi Guo, Qingli Li, Xiao-Ping Zhang

Sturge-Weber syndrome (SWS) is a vascular malformation disease, and it may cause blindness if the patient's condition is severe.

Planimation

1 code implementation11 Aug 2020 Gang Chen, Yi Ding, Hugo Edwards, Chong Hin Chau, Sai Hou, Grace Johnson, Mohammed Sharukh Syed, Haoyuan Tang, Yue Wu, Ye Yan, Gil Tidhar, Nir Lipovetzky

Planimation is a modular and extensible open source framework to visualise sequential solutions of planning problems specified in PDDL.

Improving GAN Training with Probability Ratio Clipping and Sample Reweighting

1 code implementation NeurIPS 2020 Yue Wu, Pan Zhou, Andrew Gordon Wilson, Eric P. Xing, Zhiting Hu

Despite success on a wide range of problems related to vision, generative adversarial networks (GANs) often suffer from inferior performance due to unstable training, especially for text generation.

Image Generation Style Transfer +1

Self-supervised Learning from a Multi-view Perspective

1 code implementation ICLR 2021 Yao-Hung Hubert Tsai, Yue Wu, Ruslan Salakhutdinov, Louis-Philippe Morency

In particular, we propose a composite objective that bridges the gap between prior contrastive and predictive learning objectives, and introduce an additional objective term to discard task-irrelevant information.

Image Captioning Language Modelling +3

A Finite Time Analysis of Two Time-Scale Actor Critic Methods

no code implementations4 May 2020 Yue Wu, Weitong Zhang, Pan Xu, Quanquan Gu

In this work, we provide a non-asymptotic analysis for two time-scale actor-critic methods under non-i. i. d.

Signature features with the visibility transformation

no code implementations8 Apr 2020 Yue Wu, Hao Ni, Terence J. Lyons, Robin L. Hudson

In this paper we put the visibility transformation on a clear theoretical footing and show that this transform is able to embed the effect of the absolute position of the data stream into signature features in a unified and efficient way.

Future Video Synthesis with Object Motion Prediction

1 code implementation CVPR 2020 Yue Wu, Rongrong Gao, Jaesik Park, Qifeng Chen

We present an approach to predict future video frames given a sequence of continuous video frames in the past.

Affine Transformation motion prediction +1

Cross-modality Person re-identification with Shared-Specific Feature Transfer

no code implementations CVPR 2020 Yan Lu, Yue Wu, Bin Liu, Tianzhu Zhang, Baopu Li, Qi Chu, Nenghai Yu

In this paper, we tackle the above limitation by proposing a novel cross-modality shared-specific feature transfer algorithm (termed cm-SSFT) to explore the potential of both the modality-shared information and the modality-specific characteristics to boost the re-identification performance.

Cross-Modality Person Re-identification Person Re-Identification

Towards Understanding the Spectral Bias of Deep Learning

no code implementations3 Dec 2019 Yuan Cao, Zhiying Fang, Yue Wu, Ding-Xuan Zhou, Quanquan Gu

An intriguing phenomenon observed during training neural networks is the spectral bias, which states that neural networks are biased towards learning less complex functions.

Coverage-based Outlier Explanation

no code implementations6 Nov 2019 Yue Wu, Leman Akoglu, Ian Davidson

Existing algorithms are primarily focused on detection, that is the identification of outliers in a given dataset.

Outlier Detection

ManTra-Net: Manipulation Tracing Network for Detection and Localization of Image Forgeries With Anomalous Features

2 code implementations CVPR 2019 Yue Wu, Wael AbdAlmageed, Premkumar Natarajan

To fight against real-life image forgery, which commonly involves different types and combined manipulations, we propose a unified deep neural architecture called ManTra-Net.

Anomaly Detection Fake Image Detection +2

Large Scale Incremental Learning

2 code implementations CVPR 2019 Yue Wu, Yinpeng Chen, Lijuan Wang, Yuancheng Ye, Zicheng Liu, Yandong Guo, Yun Fu

We believe this is because of the combination of two factors: (a) the data imbalance between the old and new classes, and (b) the increasing number of visually similar classes.

Incremental Learning

Unified Adversarial Invariance

no code implementations7 May 2019 Ayush Jaiswal, Yue Wu, Wael Abd-Almageed, Premkumar Natarajan

We present a unified invariance framework for supervised neural networks that can induce independence to nuisance factors of data without using any nuisance annotations, but can additionally use labeled information about biasing factors to force their removal from the latent embedding for making fair predictions.

Fairness

Rethinking Classification and Localization for Object Detection

1 code implementation CVPR 2020 Yue Wu, Yinpeng Chen, Lu Yuan, Zicheng Liu, Lijuan Wang, Hongzhi Li, Yun Fu

Two head structures (i. e. fully connected head and convolution head) have been widely used in R-CNN based detectors for classification and localization tasks.

Classification General Classification +1

QATM: Quality-Aware Template Matching For Deep Learning

2 code implementations CVPR 2019 Jiaxin Cheng, Yue Wu, Wael Abd-Almageed, Premkumar Natarajan

Finding a template in a search image is one of the core problems many computer vision, such as semantic image semantic, image-to-GPS verification \etc.

Image-To-Gps Verification Template Matching

AIRD: Adversarial Learning Framework for Image Repurposing Detection

1 code implementation CVPR 2019 Ayush Jaiswal, Yue Wu, Wael Abd-Almageed, Iacopo Masi, Premkumar Natarajan

Image repurposing is a commonly used method for spreading misinformation on social media and online forums, which involves publishing untampered images with modified metadata to create rumors and further propaganda.

Misinformation

Image-to-GPS Verification Through A Bottom-Up Pattern Matching Network

no code implementations18 Nov 2018 Jiaxin Cheng, Yue Wu, Wael Abd-Almageed, Prem Natarajan

The image-to-GPS verification problem asks whether a given image is taken at a claimed GPS location.

Image-To-Gps Verification

Towards Understanding Learning Representations: To What Extent Do Different Neural Networks Learn the Same Representation

1 code implementation NeurIPS 2018 Liwei Wang, Lunjia Hu, Jiayuan Gu, Yue Wu, Zhiqiang Hu, Kun He, John Hopcroft

The theory gives a complete characterization of the structure of neuron activation subspace matches, where the core concepts are maximum match and simple match which describe the overall and the finest similarity between sets of neurons in two networks respectively.

VelocityGAN: Data-Driven Full-Waveform Inversion Using Conditional Adversarial Networks

no code implementations26 Sep 2018 Zhongping Zhang, Yue Wu, Zheng Zhou, Youzuo Lin

Acoustic- and elastic-waveform inversion is an important and widely used method to reconstruct subsurface velocity image.

Unsupervised Adversarial Invariance

no code implementations NeurIPS 2018 Ayush Jaiswal, Yue Wu, Wael Abd-Almageed, Premkumar Natarajan

Data representations that contain all the information about target variables but are invariant to nuisance factors benefit supervised learning algorithms by preventing them from learning associations between these factors and the targets, thus reducing overfitting.

Data Augmentation Domain Adaptation +2

BusterNet: Detecting Copy-Move Image Forgery with Source/Target Localization

1 code implementation ECCV 2018 Yue Wu, Wael Abd-Almageed, Prem Natarajan

We introduce a novel deep neural architecture for image copy-move forgery detection (CMFD), code-named BusterNet.

Deep Multimodal Image-Repurposing Detection

1 code implementation20 Aug 2018 Ekraam Sabir, Wael Abd-Almageed, Yue Wu, Prem Natarajan

Nefarious actors on social media and other platforms often spread rumors and falsehoods through images whose metadata (e. g., captions) have been modified to provide visual substantiation of the rumor/falsehood.

Predictive Local Smoothness for Stochastic Gradient Methods

no code implementations ICLR 2019 Jun Li, Hongfu Liu, Bineng Zhong, Yue Wu, Yun Fu

To address this problem, we propose a simple yet effective method for improving stochastic gradient methods named predictive local smoothness (PLS).

Facial Landmark Detection: a Literature Survey

no code implementations15 May 2018 Yue Wu, Qiang Ji

The regression-based methods implicitly capture facial shape and appearance information.

Facial Landmark Detection

Machine Learning for Exam Triage

1 code implementation30 Apr 2018 Xinyu Guan, Jessica Lee, Peter Wu, Yue Wu

In this project, we extend the state-of-the-art CheXNet (Rajpurkar et al. [2017]) by making use of the additional non-image features in the dataset.

Forecasting Future Humphrey Visual Fields Using Deep Learning

2 code implementations2 Apr 2018 Joanne C. Wen, Cecilia S. Lee, Pearse A. Keane, Sa Xiao, Yue Wu, Ariel Rokem, Philip P. Chen, Aaron Y. Lee

Methods: All datapoints from consecutive 24-2 HVFs from 1998 to 2018 were extracted from a University of Washington database.

Transfer Learning

Generating retinal flow maps from structural optical coherence tomography with artificial intelligence

no code implementations24 Feb 2018 Cecilia S. Lee, Ariel J. Tyring, Yue Wu, Sa Xiao, Ariel S. Rokem, Nicolaas P. Deruyter, Qinqin Zhang, Adnan Tufail, Ruikang K. Wang, Aaron Y. Lee

Despite significant advances in artificial intelligence (AI) for computer vision, its application in medical imaging has been limited by the burden and limits of expert-generated labels.

CapsuleGAN: Generative Adversarial Capsule Network

1 code implementation17 Feb 2018 Ayush Jaiswal, Wael Abd-Almageed, Yue Wu, Premkumar Natarajan

We provide guidelines for designing CapsNet discriminators and the updated GAN objective function, which incorporates the CapsNet margin loss, for training CapsuleGAN models.

General Classification Semi-Supervised Image Classification

Incremental Classifier Learning with Generative Adversarial Networks

no code implementations2 Feb 2018 Yue Wu, Yinpeng Chen, Lijuan Wang, Yuancheng Ye, Zicheng Liu, Yandong Guo, Zhengyou Zhang, Yun Fu

To address these problems, we propose (a) a new loss function to combine the cross-entropy loss and distillation loss, (b) a simple way to estimate and remove the unbalance between the old and new classes , and (c) using Generative Adversarial Networks (GANs) to generate historical data and select representative exemplars during generation.

General Classification

MRI Tumor Segmentation with Densely Connected 3D CNN

2 code implementations18 Jan 2018 Lele Chen, Yue Wu, Adora M. DSouza, Anas Z. Abidin, Axel Wismuller, Chenliang Xu

The major difficulty of our segmentation model comes with the fact that the location, structure, and shape of gliomas vary significantly among different patients.

Tumor Segmentation

Seismic-Net: A Deep Densely Connected Neural Network to Detect Seismic Events

no code implementations17 Jan 2018 Yue Wu, Youzuo Lin, Zheng Zhou, Andrew Delorey

In particular, we demonstrate the efficacy of our Seismic-Net by formulating our detection problem as an event detection problem with time series data.

Event Detection Time Series

Clustering with Outlier Removal

no code implementations5 Jan 2018 Hongfu Liu, Jun Li, Yue Wu, Yun Fu

Then an objective function based Holoentropy is designed to enhance the compactness of each cluster with a few outliers removed.

Outlier Detection

Bidirectional Conditional Generative Adversarial Networks

no code implementations20 Nov 2017 Ayush Jaiswal, Wael Abd-Almageed, Yue Wu, Premkumar Natarajan

Conditional Generative Adversarial Networks (cGANs) are generative models that can produce data samples ($x$) conditioned on both latent variables ($z$) and known auxiliary information ($c$).

Self-Organized Text Detection With Minimal Post-Processing via Border Learning

2 code implementations ICCV 2017 Yue Wu, Prem Natarajan

In this paper we propose a new solution to the text detection problem via border learning.

Simultaneous Facial Landmark Detection, Pose and Deformation Estimation under Facial Occlusion

no code implementations CVPR 2017 Yue Wu, Chao Gou, Qiang Ji

Facial landmark detection, head pose estimation, and facial deformation analysis are typical facial behavior analysis tasks in computer vision.

Facial Landmark Detection Head Pose Estimation

Robust Facial Landmark Detection under Significant Head Poses and Occlusion

no code implementations ICCV 2015 Yue Wu, Qiang Ji

In this work, we propose a unified robust cascade regression framework that can handle both images with severe occlusion and images with large head poses.

Facial Landmark Detection Occlusion Estimation

Constrained Deep Transfer Feature Learning and its Applications

no code implementations CVPR 2016 Yue Wu, Qiang Ji

Furthermore, we propose to exploit the target domain knowledge and incorporate such prior knowledge as a constraint during transfer learning to ensure that the transferred data satisfies certain properties of the target domain.

Facial Expression Recognition Transfer Learning

Constrained Joint Cascade Regression Framework for Simultaneous Facial Action Unit Recognition and Facial Landmark Detection

no code implementations CVPR 2016 Yue Wu, Qiang Ji

Experimental results demonstrate that the intertwined relationships of facial action units and face shapes boost the performances of both facial action unit recognition and facial landmark detection.

Facial Action Unit Detection Facial Landmark Detection

A Hierarchical Probabilistic Model for Facial Feature Detection

no code implementations CVPR 2014 Yue Wu, Ziheng Wang, Qiang Ji

Facial feature detection from facial images has attracted great attention in the field of computer vision.

Facial Feature Tracking under Varying Facial Expressions and Face Poses based on Restricted Boltzmann Machines

no code implementations CVPR 2013 Yue Wu, Zuoguan Wang, Qiang Ji

To handle pose variations, the frontal face shape prior model is incorporated into a 3-way RBM model that could capture the relationship between frontal face shapes and non-frontal face shapes.

Cascaded Region-based Densely Connected Network for Event Detection: A Seismic Application

no code implementations12 Sep 2017 Yue Wu, Youzuo Lin, Zheng Zhou, David Chas Bolton, Ji Liu, Paul Johnson

Because of the fact that some positive events are not correctly annotated, we further formulate the detection problem as a learning-from-noise problem.

2D Object Detection Abnormal Event Detection In Video +2

Deep Matching and Validation Network -- An End-to-End Solution to Constrained Image Splicing Localization and Detection

no code implementations27 May 2017 Yue Wu, Wael Abd-Almageed, Prem Natarajan

Here the task is to estimate the probability that the donor image has been used to splice the query image, and obtain the splicing masks for both the query and donor images.

Image Manipulation

SOL: A Library for Scalable Online Learning Algorithms

1 code implementation28 Oct 2016 Yue Wu, Steven C. H. Hoi, Chenghao Liu, Jing Lu, Doyen Sahoo, Nenghai Yu

SOL is an open-source library for scalable online learning algorithms, and is particularly suitable for learning with high-dimensional data.

General Classification Multi-class Classification

Families in the Wild (FIW): Large-Scale Kinship Image Database and Benchmarks

no code implementations7 Apr 2016 Joseph P. Robinson, Ming Shao, Yue Wu, Yun Fu

Motivated by the lack of a single, unified dataset for kinship recognition, we aim to provide a dataset that captivates the interest of the research community.

Fine-tuning Metric Learning

Relaxing From Vocabulary: Robust Weakly-Supervised Deep Learning for Vocabulary-Free Image Tagging

no code implementations ICCV 2015 Jianlong Fu, Yue Wu, Tao Mei, Jinqiao Wang, Hanqing Lu, Yong Rui

The development of deep learning has empowered machines with comparable capability of recognizing limited image categories to human beings.

LOGO-Net: Large-scale Deep Logo Detection and Brand Recognition with Deep Region-based Convolutional Networks

no code implementations8 Nov 2015 Steven C. H. Hoi, Xiongwei Wu, Hantang Liu, Yue Wu, Huiqiong Wang, Hui Xue, Qiang Wu

In this paper, we introduce "LOGO-Net", a large-scale logo image database for logo detection and brand recognition from real-world product images.

Logo Recognition Object Detection

Learning Document Image Binarization from Data

no code implementations4 May 2015 Yue Wu, Stephen Rawls, Wael Abd-Almageed, Premkumar Natarajan

In this paper we present a fully trainable binarization solution for degraded document images.

Binarization Document Binarization

Large-scale Online Feature Selection for Ultra-high Dimensional Sparse Data

no code implementations27 Sep 2014 Yue Wu, Steven C. H. Hoi, Tao Mei, Nenghai Yu

However, unlike many second-order learning methods that often suffer from extra high computational cost, we devise a novel smart algorithm for second-order online feature selection using a MaxHeap-based approach, which is not only more effective than the existing first-order approaches, but also significantly more efficient and scalable for large-scale feature selection with ultra-high dimensional sparse data, as validated from our extensive experiments.

Feature Selection

Gaussian Process Volatility Model

no code implementations NeurIPS 2014 Yue Wu, Jose Miguel Hernandez Lobato, Zoubin Ghahramani

A Gaussian Process (GP) defines a distribution over functions, which allows us to capture highly flexible functional relationships for the variances.

Gaussian Processes

Blockwise SURE Shrinkage for Non-Local Means

no code implementations18 May 2013 Yue Wu, Brian Tracey, Premkumar Natarajan, Joseph P. Noonan

In this letter, we investigate the shrinkage problem for the non-local means (NLM) image denoising.

Image Denoising SSIM

Dynamic Covariance Models for Multivariate Financial Time Series

no code implementations18 May 2013 Yue Wu, José Miguel Hernández-Lobato, Zoubin Ghahramani

The accurate prediction of time-changing covariances is an important problem in the modeling of multivariate financial data.

Time Series

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