Search Results for author: Yuanyuan Liu

Found 39 papers, 3 papers with code

Pose-disentangled Contrastive Learning for Self-supervised Facial Representation

no code implementations24 Nov 2022 Yuanyuan Liu, Wenbin Wang, Yibing Zhan, Zhe Chen, Shaoze Feng, Kejun Liu

Self-supervised facial representation has recently attracted increasing attention due to its ability to perform face understanding without relying on large-scale annotated datasets heavily.

Contrastive Learning Data Augmentation +3

PCDNF: Revisiting Learning-based Point Cloud Denoising via Joint Normal Filtering

no code implementations2 Sep 2022 Zheng Liu, Sijing Zhan, Yaowu Zhao, Yuanyuan Liu, Renjie Chen, Ying He

Motivated by the essential interplay between point cloud denoising and normal filtering, we revisit point cloud denoising from a multitask perspective, and propose an end-to-end network, named PCDNF, to denoise point clouds via joint normal filtering.


K-UNN: k-Space Interpolation With Untrained Neural Network

1 code implementation11 Aug 2022 Zhuo-Xu Cui, Sen Jia, Qingyong Zhu, Congcong Liu, Zhilang Qiu, Yuanyuan Liu, Jing Cheng, Haifeng Wang, Yanjie Zhu, Dong Liang

Recently, untrained neural networks (UNNs) have shown satisfactory performances for MR image reconstruction on random sampling trajectories without using additional full-sampled training data.

Image Reconstruction

MAFW: A Large-scale, Multi-modal, Compound Affective Database for Dynamic Facial Expression Recognition in the Wild

no code implementations1 Aug 2022 Yuanyuan Liu, Wei Dai, Chuanxu Feng, Wenbin Wang, Guanghao Yin, Jiabei Zeng, Shiguang Shan

To the best of our knowledge, MAFW is the first in-the-wild multi-modal database annotated with compound emotion annotations and emotion-related captions.

Facial Expression Recognition (FER)

Construction of Lyapunov Functions Using Vector Field Decomposition

no code implementations13 Jul 2022 Yuanyuan Liu

In the present paper, a novel vector field decomposition based approach for constructing Lyapunov functions is proposed.

A Simple Novel Global Optimization Algorithm and Its Performance on Some Benchmark Functions

no code implementations13 Jul 2022 Yuanyuan Liu

If a global minimum is kept in the remaining region of each iteration, then it can be located with an arbitrary precision.

Dual Perceptual Loss for Single Image Super-Resolution Using ESRGAN

no code implementations17 Jan 2022 Jie Song, Huawei Yi, Wenqian Xu, Xiaohui Li, Bo Li, Yuanyuan Liu

The proposal of perceptual loss solves the problem that per-pixel difference loss function causes the reconstructed image to be overly-smooth, which acquires a significant progress in the field of single image super-resolution reconstruction.

Image Super-Resolution

Equilibrated Zeroth-Order Unrolled Deep Networks for Accelerated MRI

no code implementations18 Dec 2021 Zhuo-Xu Cui, Jing Cheng, Qingyong Zhu, Yuanyuan Liu, Sen Jia, Kankan Zhao, Ziwen Ke, Wenqi Huang, Haifeng Wang, Yanjie Zhu, Dong Liang

Specifically, focusing on accelerated MRI, we unroll a zeroth-order algorithm, of which the network module represents the regularizer itself, so that the network output can be still covered by the regularization model.

MRI Reconstruction

Expression Snippet Transformer for Robust Video-based Facial Expression Recognition

no code implementations17 Sep 2021 Yuanyuan Liu, Wenbin Wang, Chuanxu Feng, Haoyu Zhang, Zhe Chen, Yibing Zhan

To this end, we propose to decompose each video into a series of expression snippets, each of which contains a small number of facial movements, and attempt to augment the Transformer's ability for modeling intra-snippet and inter-snippet visual relations, respectively, obtaining the Expression snippet Transformer (EST).

Facial Expression Recognition (FER)

Language-Independent Approach for Automatic Computation of Vowel Articulation Features in Dysarthric Speech Assessment

1 code implementation16 Aug 2021 Yuanyuan Liu, Nelly Penttilä, Tiina Ihalainen, Juulia Lintula, Rachel Convey, Okko Räsänen

Experimental results on a Finnish PD speech corpus demonstrate the efficacy and reliability of the proposed automatic method in deriving VAI, VSA, FCR and F2i/F2u (the second formant ratio for vowels /i/ and /u/).

Learned Interpretable Residual Extragradient ISTA for Sparse Coding

no code implementations22 Jun 2021 Lin Kong, Wei Sun, Fanhua Shang, Yuanyuan Liu, Hongying Liu

Recently, the study on learned iterative shrinkage thresholding algorithm (LISTA) has attracted increasing attentions.

Large Motion Video Super-Resolution with Dual Subnet and Multi-Stage Communicated Upsampling

no code implementations22 Mar 2021 Hongying Liu, Peng Zhao, Zhubo Ruan, Fanhua Shang, Yuanyuan Liu

In this paper, we propose a novel deep neural network with Dual Subnet and Multi-stage Communicated Upsampling (DSMC) for super-resolution of videos with large motion.

Motion Compensation Motion Estimation +1

Differentially Private ADMM Algorithms for Machine Learning

no code implementations31 Oct 2020 Tao Xu, Fanhua Shang, Yuanyuan Liu, Hongying Liu, Longjie Shen, Maoguo Gong

For smooth convex loss functions with (non)-smooth regularization, we propose the first differentially private ADMM (DP-ADMM) algorithm with performance guarantee of $(\epsilon,\delta)$-differential privacy ($(\epsilon,\delta)$-DP).

BIG-bench Machine Learning

A Single Frame and Multi-Frame Joint Network for 360-degree Panorama Video Super-Resolution

2 code implementations24 Aug 2020 Hongying Liu, Zhubo Ruan, Chaowei Fang, Peng Zhao, Fanhua Shang, Yuanyuan Liu, Lijun Wang

Spherical videos, also known as \ang{360} (panorama) videos, can be viewed with various virtual reality devices such as computers and head-mounted displays.

Video Super-Resolution

Video Super Resolution Based on Deep Learning: A Comprehensive Survey

no code implementations25 Jul 2020 Hongying Liu, Zhubo Ruan, Peng Zhao, Chao Dong, Fanhua Shang, Yuanyuan Liu, Linlin Yang, Radu Timofte

To the best of our knowledge, this work is the first systematic review on VSR tasks, and it is expected to make a contribution to the development of recent studies in this area and potentially deepen our understanding to the VSR techniques based on deep learning.

speech-recognition Speech Recognition +1

Efficient High-Dimensional Data Representation Learning via Semi-Stochastic Block Coordinate Descent Methods

no code implementations25 Sep 2019 Bingkun Wei, Yangyang Li, Fanhua Shang, Yuanyuan Liu, Hongying Liu, ShengMei Shen

To address this issue, we propose a novel hard thresholding algorithm, called Semi-stochastic Block Coordinate Descent Hard Thresholding Pursuit (SBCD-HTP).

Face Recognition Representation Learning

Accelerated Variance Reduced Stochastic Extragradient Method for Sparse Machine Learning Problems

no code implementations25 Sep 2019 Fanhua Shang, Lin Kong, Yuanyuan Liu, Hua Huang, Hongying Liu

Moreover, our theoretical analysis shows that AVR-SExtraGD enjoys the best-known convergence rates and oracle complexities of stochastic first-order algorithms such as Katyusha for both strongly convex and non-strongly convex problems.

BIG-bench Machine Learning Face Recognition +1

Pose-adaptive Hierarchical Attention Network for Facial Expression Recognition

no code implementations24 May 2019 Yuanyuan Liu, Jiyao Peng, Jiabei Zeng, Shiguang Shan

Multi-view facial expression recognition (FER) is a challenging task because the appearance of an expression varies in poses.

Facial Expression Recognition (FER)

Parallel-tempered Stochastic Gradient Hamiltonian Monte Carlo for Approximate Multimodal Posterior Sampling

no code implementations4 Dec 2018 Rui Luo, Qiang Zhang, Yuanyuan Liu

We propose a new sampler that integrates the protocol of parallel tempering with the Nos\'e-Hoover (NH) dynamics.

Benchmarking Deep Sequential Models on Volatility Predictions for Financial Time Series

no code implementations8 Nov 2018 Qiang Zhang, Rui Luo, Yaodong Yang, Yuanyuan Liu

As an indicator of the level of risk or the degree of variation, volatility is important to analyse the financial market, and it is taken into consideration in various decision-making processes in financial activities.

Benchmarking Decision Making +1

Bilinear Factor Matrix Norm Minimization for Robust PCA: Algorithms and Applications

no code implementations11 Oct 2018 Fanhua Shang, James Cheng, Yuanyuan Liu, Zhi-Quan Luo, Zhouchen Lin

The heavy-tailed distributions of corrupted outliers and singular values of all channels in low-level vision have proven effective priors for many applications such as background modeling, photometric stereo and image alignment.

Moving Object Detection object-detection

Tractable and Scalable Schatten Quasi-Norm Approximations for Rank Minimization

no code implementations28 Feb 2018 Fanhua Shang, Yuanyuan Liu, James Cheng

The Schatten quasi-norm was introduced to bridge the gap between the trace norm and rank function.

Guaranteed Sufficient Decrease for Stochastic Variance Reduced Gradient Optimization

no code implementations26 Feb 2018 Fanhua Shang, Yuanyuan Liu, Kaiwen Zhou, James Cheng, Kelvin K. W. Ng, Yuichi Yoshida

In order to make sufficient decrease for stochastic optimization, we design a new sufficient decrease criterion, which yields sufficient decrease versions of stochastic variance reduction algorithms such as SVRG-SD and SAGA-SD as a byproduct.

Stochastic Optimization

Accelerated First-order Methods for Geodesically Convex Optimization on Riemannian Manifolds

no code implementations NeurIPS 2017 Yuanyuan Liu, Fanhua Shang, James Cheng, Hong Cheng, Licheng Jiao

In this paper, we propose an accelerated first-order method for geodesically convex optimization, which is the generalization of the standard Nesterov's accelerated method from Euclidean space to nonlinear Riemannian space.

Accelerated Variance Reduced Stochastic ADMM

no code implementations11 Jul 2017 Yuanyuan Liu, Fanhua Shang, James Cheng

Besides having a low per-iteration complexity as existing stochastic ADMM methods, ASVRG-ADMM improves the convergence rate on general convex problems from O(1/T) to O(1/T^2).

Adversarial Variational Bayes Methods for Tweedie Compound Poisson Mixed Models

no code implementations16 Jun 2017 Yaodong Yang, Rui Luo, Yuanyuan Liu

Mixed models with random effects account for the covariance structure related to the grouping hierarchy in the data.

Variational Inference

Fast Stochastic Variance Reduced Gradient Method with Momentum Acceleration for Machine Learning

no code implementations23 Mar 2017 Fanhua Shang, Yuanyuan Liu, James Cheng, Jiacheng Zhuo

Recently, research on accelerated stochastic gradient descent methods (e. g., SVRG) has made exciting progress (e. g., linear convergence for strongly convex problems).

BIG-bench Machine Learning regression

Guaranteed Sufficient Decrease for Variance Reduced Stochastic Gradient Descent

no code implementations20 Mar 2017 Fanhua Shang, Yuanyuan Liu, James Cheng, Kelvin Kai Wing Ng, Yuichi Yoshida

In order to make sufficient decrease for stochastic optimization, we design a new sufficient decrease criterion, which yields sufficient decrease versions of variance reduction algorithms such as SVRG-SD and SAGA-SD as a byproduct.

Stochastic Optimization

Scalable Algorithms for Tractable Schatten Quasi-Norm Minimization

no code implementations4 Jun 2016 Fanhua Shang, Yuanyuan Liu, James Cheng

In this paper, we first define two tractable Schatten quasi-norms, i. e., the Frobenius/nuclear hybrid and bi-nuclear quasi-norms, and then prove that they are in essence the Schatten-2/3 and 1/2 quasi-norms, respectively, which lead to the design of very efficient algorithms that only need to update two much smaller factor matrices.

Matrix Completion

Unified Scalable Equivalent Formulations for Schatten Quasi-Norms

no code implementations2 Jun 2016 Fanhua Shang, Yuanyuan Liu, James Cheng

In this paper, we rigorously prove that for any p, p1, p2>0 satisfying 1/p=1/p1+1/p2, the Schatten-p quasi-norm of any matrix is equivalent to minimizing the product of the Schatten-p1 norm (or quasi-norm) and Schatten-p2 norm (or quasi-norm) of its two factor matrices.

Generalized Higher-Order Orthogonal Iteration for Tensor Decomposition and Completion

no code implementations NeurIPS 2014 Yuanyuan Liu, Fanhua Shang, Wei Fan, James Cheng, Hong Cheng

Then the Schatten 1-norm of the core tensor is used to replace that of the whole tensor, which leads to a much smaller-scale matrix SNM problem.

Tensor Decomposition

Structured Low-Rank Matrix Factorization with Missing and Grossly Corrupted Observations

no code implementations3 Sep 2014 Fanhua Shang, Yuanyuan Liu, Hanghang Tong, James Cheng, Hong Cheng

In this paper, we propose a scalable, provable structured low-rank matrix factorization method to recover low-rank and sparse matrices from missing and grossly corrupted data, i. e., robust matrix completion (RMC) problems, or incomplete and grossly corrupted measurements, i. e., compressive principal component pursuit (CPCP) problems.

Matrix Completion

Generalized Higher-Order Tensor Decomposition via Parallel ADMM

no code implementations5 Jul 2014 Fanhua Shang, Yuanyuan Liu, James Cheng

To address these problems, we first propose a parallel trace norm regularized tensor decomposition method, and formulate it as a convex optimization problem.

Tensor Decomposition

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