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Found 50 papers, 9 papers with code
Bayesian imaging inverse problem with SA-Roundtrip prior via HMC-pCN sampler
Bayesian inference with deep generative prior has received considerable interest for solving imaging inverse problems in many scientific and engineering fields.
Learning Language-guided Adaptive Hyper-modality Representation for Multimodal Sentiment Analysis
Though Multimodal Sentiment Analysis (MSA) proves effective by utilizing rich information from multiple sources (e. g., language, video, and audio), the potential sentiment-irrelevant and conflicting information across modalities may hinder the performance from being further improved.
Ranked #1 on
Multimodal Sentiment Analysis
on CMU-MOSEI
(Acc-7 metric)
Score-based Diffusion Models With Self-supervised Learning For Accelerated 3D Multi-contrast Cardiac Magnetic Resonance Imaging
Long scan time significantly hinders the widespread applications of three-dimensional multi-contrast cardiac magnetic resonance (3D-MC-CMR) imaging.
Physics-Informed DeepMRI: Bridging the Gap from Heat Diffusion to k-Space Interpolation
In order to enhance interpretability and overcome the acceleration limitations, this paper introduces an interpretable framework that unifies both $k$-space interpolation techniques and image-domain methods, grounded in the physical principles of heat diffusion equations.
Improving the Transferability of Adversarial Examples with Arbitrary Style Transfer
Deep neural networks are vulnerable to adversarial examples crafted by applying human-imperceptible perturbations on clean inputs.
A Hierarchical Destroy and Repair Approach for Solving Very Large-Scale Travelling Salesman Problem
Fair comparisons based on nineteen famous large-scale instances (with 10, 000 to 10, 000, 000 cities) show that HDR is highly competitive against existing state-of-the-art TSP algorithms, in terms of both efficiency and solution quality.
A Fast Fourier Convolutional Deep Neural Network For Accurate and Explainable Discrimination Of Wheat Yellow Rust And Nitrogen Deficiency From Sentinel-2 Time-Series Data
Specifically, unlike the existing CNN models, the main components of the proposed model include: 1) a fast Fourier convolutional block, a newly fast Fourier transformation kernel as the basic perception unit, to substitute the traditional convolutional kernel to capture both local and global responses to plant stress in various time-scale and improve computing efficiency with reduced learning parameters in Fourier domain; 2) Capsule Feature Encoder to encapsulate the extracted features into a series of vector features to represent part-to-whole relationship with the hierarchical structure of the host-stress interactions of the specific stress.
Boosting Adversarial Transferability by Achieving Flat Local Maxima
Extensive experimental results on the ImageNet-compatible dataset show that the proposed method can generate adversarial examples at flat local regions, and significantly improve the adversarial transferability on either normally trained models or adversarially trained models than the state-of-the-art attacks.
Meta-Learning Enabled Score-Based Generative Model for 1.5T-Like Image Reconstruction from 0.5T MRI
We theoretically uncovered that the combination of these challenges renders conventional deep learning methods that directly learn the mapping from a low-field MR image to a high-field MR image unsuitable.
Noise-Resistant Multimodal Transformer for Emotion Recognition
To this end, we present a novel paradigm that attempts to extract noise-resistant features in its pipeline and introduces a noise-aware learning scheme to effectively improve the robustness of multimodal emotion understanding.
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.
Ranked #9 on
Dynamic Facial Expression Recognition
on MAFW
Dynamic Facial Expression Recognition
Facial Expression Recognition
+1
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.
Clip-aware expressive feature learning for video-based facial expression recognition
In this paper, we divide a video into several short clips for processing and propose a clip-aware emotion-rich feature learning network (CEFLNet) for robust video-based FER.
Ranked #16 on
Dynamic Facial Expression Recognition
on DFEW
Dynamic Facial Expression Recognition
Facial Expression Recognition
+1
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).
Ranked #14 on
Dynamic Facial Expression Recognition
on DFEW
Dynamic Facial Expression Recognition
Facial Expression Recognition
+1
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.
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.
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).
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.
Facial Expression Recognition
Facial Expression Recognition (FER)
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.
