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Found 41 papers, 12 papers with code
Human Video Translation via Query Warping
In this paper, we present QueryWarp, a novel framework for temporally coherent human motion video translation.
DiffusionMat: Alpha Matting as Sequential Refinement Learning
In this paper, we introduce DiffusionMat, a novel image matting framework that employs a diffusion model for the transition from coarse to refined alpha mattes.
Damped Proximal Augmented Lagrangian Method for weakly-Convex Problems with Convex Constraints
For the former case, DPALM achieves the complexity of $\widetilde{\mathcal{O}}\left(\varepsilon^{-2. 5} \right)$ to produce an $\varepsilon$-KKT point by applying an accelerated proximal gradient (APG) method to each DPALM subproblem.
Spectral Adversarial MixUp for Few-Shot Unsupervised Domain Adaptation
To accomplish this challenging task, first, a spectral sensitivity map is introduced to characterize the generalization weaknesses of models in the frequency domain.
RIGID: Recurrent GAN Inversion and Editing of Real Face Videos
In this paper, we propose a unified recurrent framework, named \textbf{R}ecurrent v\textbf{I}deo \textbf{G}AN \textbf{I}nversion and e\textbf{D}iting (RIGID), to explicitly and simultaneously enforce temporally coherent GAN inversion and facial editing of real videos.
Deformable Mixer Transformer with Gating for Multi-Task Learning of Dense Prediction
In this work, we present a novel MTL model by combining both merits of deformable CNN and query-based Transformer with shared gating for multi-task learning of dense prediction.
First-order Methods for Affinely Constrained Composite Non-convex Non-smooth Problems: Lower Complexity Bound and Near-optimal Methods
In this paper, we make the first attempt to establish lower complexity bounds of FOMs for solving a class of composite non-convex non-smooth optimization with linear constraints.
Variance-reduced accelerated methods for decentralized stochastic double-regularized nonconvex strongly-concave minimax problems
Coupling this with variance-reduction (VR) techniques, our proposed method, entitled VRLM, by a single neighbor communication per iteration, is able to achieve an $\mathcal{O}(\kappa^3\varepsilon^{-3})$ sample complexity under the general stochastic setting, with either a big-batch or small-batch VR option, where $\kappa$ is the condition number of the problem and $\varepsilon$ is the desired solution accuracy.
Decentralized gradient descent maximization method for composite nonconvex strongly-concave minimax problems
With this relation, we show that when the dual regularizer is smooth, our algorithm can have lower complexity results (with reduced dependence on a condition number) than existing ones to produce a near-stationary point of the original formulation.
DeMT: Deformable Mixer Transformer for Multi-Task Learning of Dense Prediction
In this work, we present a novel MTL model by combining both merits of deformable CNN and query-based Transformer for multi-task learning of dense prediction.
Multi-Task Learning with Knowledge Distillation for Dense Prediction
With the less sensitive divergence, our knowledge distillation with an alternative match is applied for capturing inter-task and intra-task information between the teacher model and the student model of each task, thereby learning more "dark knowledge" for effective distillation.
Stochastic Inexact Augmented Lagrangian Method for Nonconvex Expectation Constrained Optimization
In this paper, we design and analyze stochastic inexact augmented Lagrangian methods (Stoc-iALM) to solve problems involving a nonconvex composite (i. e. smooth+nonsmooth) objective and nonconvex smooth functional constraints.
When Neural Networks Fail to Generalize? A Model Sensitivity Perspective
Domain generalization (DG) aims to train a model to perform well in unseen domains under different distributions.
Hybrid Multimodal Feature Extraction, Mining and Fusion for Sentiment Analysis
In this paper, we present our solutions for the Multimodal Sentiment Analysis Challenge (MuSe) 2022, which includes MuSe-Humor, MuSe-Reaction and MuSe-Stress Sub-challenges.
Multi-Task Learning with Multi-Query Transformer for Dense Prediction
We first model each task with a task-relevant query.
High-resolution Face Swapping via Latent Semantics Disentanglement
Although previous research can leverage generative priors to produce high-resolution results, their quality can suffer from the entangled semantics of the latent space.
From Continuity to Editability: Inverting GANs with Consecutive Images
This inborn property is used for two unique purposes: 1) regularizing the joint inversion process, such that each of the inverted code is semantically accessible from one of the other and fastened in a editable domain; 2) enforcing inter-image coherence, such that the fidelity of each inverted code can be maximized with the complement of other images.
Deep Texture-Aware Features for Camouflaged Object Detection
Camouflaged object detection is a challenging task that aims to identify objects having similar texture to the surroundings.
Momentum-based variance-reduced proximal stochastic gradient method for composite nonconvex stochastic optimization
In this paper, we propose a new SGM, named PStorm, for solving nonconvex nonsmooth stochastic problems.
Parallel and distributed asynchronous adaptive stochastic gradient methods
The proposed method is tested on both convex and non-convex machine learning problems, and the numerical results demonstrate its clear advantages over the sync counterpart and the async-parallel nonadaptive SGM.
Optimization and Control Distributed, Parallel, and Cluster Computing Numerical Analysis Numerical Analysis 90C15, 65Y05, 68W15, 65K05
Katyusha Acceleration for Convex Finite-Sum Compositional Optimization
However, the additional compositional structure prohibits easy access to unbiased stochastic approximation of the gradient, so directly applying the SGM to a finite-sum compositional optimization problem (COP) is often inefficient.
Markov Chain Block Coordinate Descent
random and cyclic selections are either infeasible or very expensive.
A Block Coordinate Ascent Algorithm for Mean-Variance Optimization
Risk management in dynamic decision problems is a primary concern in many fields, including financial investment, autonomous driving, and healthcare.
Ensemble One-dimensional Convolution Neural Networks for Skeleton-based Action Recognition
Our first subnet is a two-stream network which could explore both temporal and spatial information.
Asynchronous parallel primal-dual block coordinate update methods for affinely constrained convex programs
Recent several years have witnessed the surge of asynchronous (async-) parallel computing methods due to the extremely big data involved in many modern applications and also the advancement of multi-core machines and computer clusters.
Accelerated Primal-Dual Proximal Block Coordinate Updating Methods for Constrained Convex Optimization
We show that the rate can be accelerated to $O(1/t^2)$ if the objective is strongly convex.
On the Convergence of Asynchronous Parallel Iteration with Unbounded Delays
Recent years have witnessed the surge of asynchronous parallel (async-parallel) iterative algorithms due to problems involving very large-scale data and a large number of decision variables.
A Primer on Coordinate Descent Algorithms
This monograph presents a class of algorithms called coordinate descent algorithms for mathematicians, statisticians, and engineers outside the field of optimization.
Hybrid Jacobian and Gauss-Seidel proximal block coordinate update methods for linearly constrained convex programming
In optimization, BCU first appears as the coordinate descent method that works well for smooth problems or those with separable nonsmooth terms and/or separable constraints.
Accelerated first-order primal-dual proximal methods for linearly constrained composite convex programming
al, SIIMS'14], which requires strong convexity on both block variables and no linearization to the objective or augmented term.
Randomized Primal-Dual Proximal Block Coordinate Updates
Assuming mere convexity, we establish its $O(1/t)$ convergence rate in terms of the objective value and feasibility measure.
Coordinate Friendly Structures, Algorithms and Applications
To derive simple subproblems for several new classes of applications, this paper systematically studies coordinate-friendly operators that perform low-cost coordinate updates.
Proximal gradient method for huberized support vector machine
The Support Vector Machine (SVM) has been used in a wide variety of classification problems.
Alternating direction method of multipliers for regularized multiclass support vector machines
A lot of effort has been put to generalize the binary SVM to multiclass SVM (MSVM) which are more complex problems.
ARock: an Algorithmic Framework for Asynchronous Parallel Coordinate Updates
The agents share $x$ through either global memory or communication.
Global and Local Structure Preserving Sparse Subspace Learning: An Iterative Approach to Unsupervised Feature Selection
In this paper, we propose a global and local structure preserving sparse subspace learning (GLoSS) model for unsupervised feature selection.
A fast patch-dictionary method for whole image recovery
With very few exceptions, this issue has limited the applications of image-patch methods to the local kind of tasks such as denoising, inpainting, cartoon-texture decomposition, super-resolution, and image deblurring, for which one can process a few patches at a time.
Block stochastic gradient iteration for convex and nonconvex optimization
Its convergence for both convex and nonconvex cases are established in different senses.
Sparse Bilinear Logistic Regression
In this paper, we introduce the concept of sparse bilinear logistic regression for decision problems involving explanatory variables that are two-dimensional matrices.
Parallel matrix factorization for low-rank tensor completion
Phase transition plots reveal that our algorithm can recover a variety of synthetic low-rank tensors from significantly fewer samples than the compared methods, which include a matrix completion method applied to tensor recovery and two state-of-the-art tensor completion methods.
Numerical Analysis Numerical Analysis Computation
An Alternating Direction Algorithm for Matrix Completion with Nonnegative Factors
By taking the advantages of both nonnegativity and low-rankness, one can generally obtain superior results than those of just using one of the two properties.
Information Theory Numerical Analysis Information Theory Numerical Analysis
