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Found 39 papers, 3 papers with code
Pose-disentangled Contrastive Learning for Self-supervised Facial Representation
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.
Explore Contextual Information for 3D Scene Graph Generation
3D scene graph generation (SGG) has been of high interest in computer vision.
PCDNF: Revisiting Learning-based Point Cloud Denoising via Joint Normal Filtering
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
Recently, untrained neural networks (UNNs) have shown satisfactory performances for MR image reconstruction on random sampling trajectories without using additional full-sampled training data.
MAFW: A Large-scale, Multi-modal, Compound Affective Database for Dynamic Facial Expression Recognition in the Wild
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.
Accelerating Magnetic Resonance T1\r{ho} Mapping Using Simultaneously Spatial Patch-based and Parametric Group-based Low-rank Tensors (SMART)
Prospective reconstruction results further demonstrate the capability of the SMART method in accelerating MR T1\r{ho} imaging.
Construction of Lyapunov Functions Using Vector Field Decomposition
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
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
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.
Equilibrated Zeroth-Order Unrolled Deep Networks for Accelerated MRI
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.
Highly accelerated MR parametric mapping by undersampling the k-space and reducing the contrast number simultaneously with deep learning
Methods: The proposed framework consists of a reconstruction module and a generative module.
Expression Snippet Transformer for Robust Video-based Facial Expression Recognition
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).
Language-Independent Approach for Automatic Computation of Vowel Articulation Features in Dysarthric Speech Assessment
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/).
Behavior Mimics Distribution: Combining Individual and Group Behaviors for Federated Learning
Unlike existing FL methods, our IGFL can be applied to both client and server optimization.
Learned Interpretable Residual Extragradient ISTA for Sparse Coding
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
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.
Differentially Private ADMM Algorithms for Machine Learning
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).
Boosting Gradient for White-Box Adversarial Attacks
Existing white-box attack algorithms can generate powerful adversarial examples.
A Single Frame and Multi-Frame Joint Network for 360-degree Panorama Video Super-Resolution
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 Based on Deep Learning: A Comprehensive Survey
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.
Efficient Relaxed Gradient Support Pursuit for Sparsity Constrained Non-convex Optimization
Large-scale non-convex sparsity-constrained problems have recently gained extensive attention.
Efficient High-Dimensional Data Representation Learning via Semi-Stochastic Block Coordinate Descent Methods
To address this issue, we propose a novel hard thresholding algorithm, called Semi-stochastic Block Coordinate Descent Hard Thresholding Pursuit (SBCD-HTP).
Accelerated Variance Reduced Stochastic Extragradient Method for Sparse Machine Learning Problems
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.
Pose-adaptive Hierarchical Attention Network for Facial Expression Recognition
Multi-view facial expression recognition (FER) is a challenging task because the appearance of an expression varies in poses.
Parallel-tempered Stochastic Gradient Hamiltonian Monte Carlo for Approximate Multimodal Posterior Sampling
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
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.
Bilinear Factor Matrix Norm Minimization for Robust PCA: Algorithms and Applications
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.
Tractable and Scalable Schatten Quasi-Norm Approximations for Rank Minimization
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
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.
Accelerated First-order Methods for Geodesically Convex Optimization on Riemannian Manifolds
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
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
Mixed models with random effects account for the covariance structure related to the grouping hierarchy in the data.
Fast Stochastic Variance Reduced Gradient Method with Momentum Acceleration for Machine Learning
Recently, research on accelerated stochastic gradient descent methods (e. g., SVRG) has made exciting progress (e. g., linear convergence for strongly convex problems).
Guaranteed Sufficient Decrease for Variance Reduced Stochastic Gradient Descent
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.
Scalable Algorithms for Tractable Schatten Quasi-Norm Minimization
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.
Unified Scalable Equivalent Formulations for Schatten Quasi-Norms
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
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.
Structured Low-Rank Matrix Factorization with Missing and Grossly Corrupted Observations
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.
Generalized Higher-Order Tensor Decomposition via Parallel ADMM
To address these problems, we first propose a parallel trace norm regularized tensor decomposition method, and formulate it as a convex optimization problem.
