Search Results for author: Yue Wu

Found 111 papers, 37 papers with code

A Neural RDE-based model for solving path-dependent PDEs

no code implementations1 Jun 2023 Bowen Fang, Hao Ni, Yue Wu

The concept of the path-dependent partial differential equation (PPDE) was first introduced in the context of path-dependent derivatives in financial markets.

NODE-ImgNet: a PDE-informed effective and robust model for image denoising

1 code implementation18 May 2023 Xinheng Xie, Yue Wu, Hao Ni, Cuiyu He

Inspired by the traditional partial differential equation (PDE) approach for image denoising, we propose a novel neural network architecture, referred as NODE-ImgNet, that combines neural ordinary differential equations (NODEs) with convolutional neural network (CNN) blocks.

Image Denoising

Multi-spectral Class Center Network for Face Manipulation Detection and Localization

no code implementations18 May 2023 Changtao Miao, Qi Chu, Zhentao Tan, Zhenchao Jin, Wanyi Zhuang, Yue Wu, Bin Liu, Honggang Hu, Nenghai Yu

Next, a novel Multi-Spectral Class Center Network (MSCCNet) is proposed for face manipulation detection and localization.

Face Swapping

Uniform-PAC Guarantees for Model-Based RL with Bounded Eluder Dimension

no code implementations15 May 2023 Yue Wu, Jiafan He, Quanquan Gu

Recently, there has been remarkable progress in reinforcement learning (RL) with general function approximation.

Reinforcement Learning (RL)

Personalized Federated Learning under Mixture of Distributions

no code implementations1 May 2023 Yue Wu, Shuaicheng Zhang, Wenchao Yu, Yanchi Liu, Quanquan Gu, Dawei Zhou, Haifeng Chen, Wei Cheng

The recent trend towards Personalized Federated Learning (PFL) has garnered significant attention as it allows for the training of models that are tailored to each client while maintaining data privacy.

Personalized Federated Learning

Score-based Transport Modeling for Mean-Field Fokker-Planck Equations

no code implementations21 Apr 2023 Jianfeng Lu, Yue Wu, Yang Xiang

We use the score-based transport modeling method to solve the mean-field Fokker-Planck equations, which we call MSBTM.

Elastic Interaction Energy-Based Generative Model: Approximation in Feature Space

no code implementations19 Mar 2023 Chuqi Chen, Yue Wu, Yang Xiang

We adopt the GAN framework and replace the discriminator with a feature transformation network to map the data into a latent space.

Borda Regret Minimization for Generalized Linear Dueling Bandits

no code implementations15 Mar 2023 Yue Wu, Tao Jin, Hao Lou, Farzad Farnoud, Quanquan Gu

Surprisingly, the Borda regret minimization problem turns out to be difficult, as we prove a regret lower bound of order $\Omega(d^{2/3} T^{2/3})$, where $d$ is the dimension of contextual vectors and $T$ is the time horizon.

Recommendation Systems

Video Waterdrop Removal via Spatio-Temporal Fusion in Driving Scenes

1 code implementation12 Feb 2023 Qiang Wen, Yue Wu, Qifeng Chen

The waterdrops on windshields during driving can cause severe visual obstructions, which may lead to car accidents.

Autonomous Driving

Read and Reap the Rewards: Learning to Play Atari with the Help of Instruction Manuals

no code implementations9 Feb 2023 Yue Wu, Yewen Fan, Paul Pu Liang, Amos Azaria, Yuanzhi Li, Tom M. Mitchell

Read and Reward speeds up RL algorithms on Atari games by reading manuals released by the Atari game developers.

Atari Games

Avoiding spurious correlations via logit correction

1 code implementation2 Dec 2022 Sheng Liu, Xu Zhang, Nitesh Sekhar, Yue Wu, Prateek Singhal, Carlos Fernandez-Granda

Empirical studies suggest that machine learning models trained with empirical risk minimization (ERM) often rely on attributes that may be spuriously correlated with the class labels.

How to Describe Images in a More Funny Way? Towards a Modular Approach to Cross-Modal Sarcasm Generation

no code implementations20 Nov 2022 Jie Ruan, Yue Wu, Xiaojun Wan, Yuesheng Zhu

Sarcasm generation has been investigated in previous studies by considering it as a text-to-text generation problem, i. e., generating a sarcastic sentence for an input sentence.

Text Generation

Bayesian Layer Graph Convolutioanl Network for Hyperspetral Image Classification

no code implementations14 Nov 2022 Mingyang Zhang, Ziqi Di, Maoguo Gong, Yue Wu, Hao Li, Xiangming Jiang

In recent years, research on hyperspectral image (HSI) classification has continuous progress on introducing deep network models, and recently the graph convolutional network (GCN) based models have shown impressive performance.

Classification Image Classification

Sybil-Proof Diffusion Auction in Social Networks

no code implementations3 Nov 2022 Hongyin Chen, Xiaotie Deng, Ying Wang, Yue Wu, Dengji Zhao

A diffusion auction is a market to sell commodities over a social network, where the challenge is to incentivize existing buyers to invite their neighbors in the network to join the market.

AniFaceGAN: Animatable 3D-Aware Face Image Generation for Video Avatars

no code implementations12 Oct 2022 Yue Wu, Yu Deng, Jiaolong Yang, Fangyun Wei, Qifeng Chen, Xin Tong

To achieve meaningful control over facial expressions via deformation, we propose a 3D-level imitative learning scheme between the generator and a parametric 3D face model during adversarial training of the 3D-aware GAN.

Disentanglement Face Model +1

Towards Consistency and Complementarity: A Multiview Graph Information Bottleneck Approach

1 code implementation11 Oct 2022 Xiaolong Fan, Maoguo Gong, Yue Wu, Mingyang Zhang, Hao Li, Xiangming Jiang

In this paper, we propose a novel Multiview Variational Graph Information Bottleneck (MVGIB) principle to maximize the agreement for common representations and the disagreement for view-specific representations.

Towards Understanding Mixture of Experts in Deep Learning

1 code implementation4 Aug 2022 Zixiang Chen, Yihe Deng, Yue Wu, Quanquan Gu, Yuanzhi Li

To our knowledge, this is the first result towards formally understanding the mechanism of the MoE layer for deep learning.

Graph Generative Model for Benchmarking Graph Neural Networks

1 code implementation10 Jul 2022 Minji Yoon, Yue Wu, John Palowitch, Bryan Perozzi, Ruslan Salakhutdinov

As the field of Graph Neural Networks (GNN) continues to grow, it experiences a corresponding increase in the need for large, real-world datasets to train and test new GNN models on challenging, realistic problems.

Benchmarking Graph Generation +1

Optimizing Video Prediction via Video Frame Interpolation

1 code implementation CVPR 2022 Yue Wu, Qiang Wen, Qifeng Chen

Extensive experiments on the Cityscapes, KITTI, DAVIS, Middlebury, and Vimeo90K datasets show that our video prediction results are robust in general scenarios, and our approach outperforms other video prediction methods that require a large amount of training data or extra semantic information.

Video Frame Interpolation Video Prediction

Geometric Policy Iteration for Markov Decision Processes

no code implementations12 Jun 2022 Yue Wu, Jesús A. De Loera

GPI updates the policy of a single state by switching to an action that is mapped to the boundary of the value function polytope, followed by an immediate update of the value function.

Prompt-aligned Gradient for Prompt Tuning

1 code implementation30 May 2022 Beier Zhu, Yulei Niu, Yucheng Han, Yue Wu, Hanwang Zhang

Thanks to the large pre-trained vision-language models (VLMs) like CLIP, we can craft a zero-shot classifier by "prompt", e. g., the confidence score of an image being "[CLASS]" can be obtained by using the VLM provided similarity measure between the image and the prompt sentence "a photo of a [CLASS]".

Domain Adaptation Few-Shot Learning +1

The China Trade Shock and the ESG Performances of US firms

no code implementations28 Jan 2022 Hui Xu, Yue Wu

Exploiting a trade policy in which US congress granted China the Permanent Normal Trade Relations and the resulting change in expected tariff rates on Chinese imports, we find that greater import competition from China leads to an increase in the US company's ESG performance.

FashionVLP: Vision Language Transformer for Fashion Retrieval With Feedback

no code implementations CVPR 2022 Sonam Goenka, Zhaoheng Zheng, Ayush Jaiswal, Rakesh Chada, Yue Wu, Varsha Hedau, Pradeep Natarajan

Fashion image retrieval based on a query pair of reference image and natural language feedback is a challenging task that requires models to assess fashion related information from visual and textual modalities simultaneously.

Image Retrieval Retrieval

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

1 code implementation8 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 Forecasting

Embedding Novel Views in a Single JPEG Image

1 code implementation 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 Colorization +1

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.

reinforcement-learning Reinforcement Learning (RL) +1

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.

Handwritten Text Recognition object-detection +2

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 +2

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

1 code implementation14 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.


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.

Change Detection

Style-Aware Normalized Loss for Improving Arbitrary Style Transfer

1 code implementation 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.

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.

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 +2

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.

Vocal Bursts Valence Prediction

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.

Ranked #99 on Image Classification on ObjectNet (using extra training data)

Benchmarking Class-agnostic Object Detection +4

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.


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 +4

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.

Vocal Bursts Valence Prediction

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.

Game Theoretic Consequences of Resident Matching

no code implementations12 Mar 2020 Yue Wu

The resident matching algorithm, Gale-Shapley, currently used by SF Match and the National Residency Match Program (NRMP), has been in use for over 50 years without fundamental alteration.

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

3 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

3 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.

Disentanglement Fairness

Rethinking Classification and Localization for Object Detection

2 code implementations 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 +2

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.


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.

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 Disentanglement +3

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.

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 regression

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.

BIG-bench Machine Learning

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

3 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 Analysis

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.

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 +1

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 +1

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 (FER) Transfer 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

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 +4

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.

BIG-bench Machine Learning General Classification +1

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.

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 +1

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.


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 Vocal Bursts Intensity Prediction

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 Analysis

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