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Works399 from public data
- Robust Recovery of Subspace Structures by Low-Rank Representation3,596
It is shown that the convex program associated with LRR solves the subspace clustering problem in the following sense: When the data is clean, LRR exactly recovers the true subspace structures; when the data are contaminated by outliers, it is proved that under certain conditions LRR can exactly recover the row space of the original data.
- Robust Subspace Segmentation by Low-Rank Representation1,756
Both theoretical and experimental results show that low-rank representation is a promising tool for subspace segmentation from corrupted data.
- Tensor Robust Principal Component Analysis with a New Tensor Nuclear Norm1,142
This paper considers the Tensor Robust Principal Component Analysis (TRPCA) problem, which aims to exactly recover the low-rank and sparse components from their sum, and proposes a model based on the recently proposed tensor-tensor product, which includes matrix RPCA as a special case.
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- Recurrent Squeeze-and-Excitation Context Aggregation Net for Single Image Deraining926
A novel deep network architecture based on deep convolutional and recurrent neural networks for single image deraining based on contextual information is proposed and outperforms the state-of-the-art approaches under all evaluation metrics.
- Expectation-Maximization Attention Networks for Semantic Segmentation700
This paper forms the attention mechanism into an expectation-maximization manner and iteratively estimate a much more compact set of bases upon which the attention maps are computed, which is robust to the variance of input and is also friendly in memory and computation.
- Tensor Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Tensors via Convex Optimization541
This work proves that under certain suitable assumptions, it can recover both the low-rank and the sparse components exactly by simply solving a convex program whose objective is a weighted combination of the tensor nuclear norm and the l1-norm.
- Fast Convex Optimization Algorithms for Exact Recovery of a Corrupted Low-Rank Matrix532
Two complementary approaches for solving the problem of recovering a low-rank matrix with a fraction of its entries arbitrarily corrupted are developed and compared, both several orders of magnitude faster than the previous state-of-the-art algorithm for this problem.
- Accelerated proximal gradient methods for nonconvex programming499
This paper is the first to provide APG-type algorithms for general nonconvex and nonsmooth problems ensuring that every accumulation point is a critical point, and the convergence rates remain O(1/k2) when the problems are convex.
- Subspace Clustering by Block Diagonal Representation472
With this regularizer, the subspace clustering problem is solved by Block Diagonal Representation (BDR), which uses the block diagonal structure prior and the BDR model is nonconvex and the experiments demonstrate the effectiveness of BDR.
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- Fast, automatic and fine-grained tampered JPEG image detection via DCT coefficient analysis438
This paper proposes detecting tampered images by examining the double quantization effect hidden among the discrete cosine transform (DCT) coefficients, and is the only one to date that can automatically locate the tampered region.
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- Relay Backpropagation for Effective Learning of Deep Convolutional Neural Networks421
This paper proposes a novel method Relay Backpropagation, which encourages the propagation of effective information through the network in training stage, and achieves the first place in ILSVRC 2015 Scene Classification Challenge.
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- Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models347
Extensive experimental results show that Adan consistently surpasses the corresponding SoTA optimizers on vision, language, and RL tasks and sets new SoTAs for many popular networks and frameworks, e.g., ResNet, ConvNext, ViT, Swin, MAE, DETR, GPT-2, Transformer-XL, and BERT.
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- Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labeled Nodes315
A novel training algorithm for Graph Convolutional Network, called Multi-Stage Self-Supervised (M3S) Training Algorithm, combined with self-supervised learning approach, focusing on improving the generalization performance of GCNs on graphs with few labeled nodes.
- Improving Semantic Segmentation via Decoupled Body and Edge Supervision313
This paper proposes a new paradigm for semantic segmentation that establishes new state of the art while retaining high efficiency in inference and shows that the proposed framework with various baselines or backbone networks leads to better object inner consistency and object boundaries.
- Invertible Image Rescaling306
An Invertible Rescaling Net (IRN) is developed with deliberately designed framework and objectives to produce visually-pleasing low-resolution images and meanwhile capture the distribution of the lost information using a latent variable following a specified distribution in the downscaling process.
- Generalized Nonconvex Nonsmooth Low-Rank Minimization293
In theory, it is proved that IRNN decreases the objective function value monotonically, and any limit point is a stationary point, which enhances the low-rank matrix recovery compared with state-of-the-art convex algorithms.
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- Robust Estimation of 3D Human Poses from a Single Image222
This work proposes a method of estimating 3D human poses from a single image, which works in conjunction with an existing 2D pose/joint detector, which outperforms the state-of-the-arts on three benchmark datasets.
- Generalized Singular Value Thresholding221
This work proves that GSVT can be obtained by performing the proximal operator of g on the singular values since Proxg(.) is monotone when g is lower bounded, and is able to solve the nonconvex low rank minimization problem by using GSVT in place of SVT.
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- Correlation Adaptive Subspace Segmentation by Trace Lasso213
The Correlation Adaptive Subspace Segmentation (CASS) method is proposed, a data correlation dependent method which simultaneously performs automatic data selection and groups correlated data together and can be regarded as a method which adaptively balances SSC and LSR.
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- Deep Comprehensive Correlation Mining for Image Clustering204
A novel clustering framework, named deep comprehensive correlation mining~(DCCM), for exploring and taking full advantage of various kinds of correlations behind the unlabeled data from three aspects: Instead of only using pair-wise information, pseudo-label supervision is proposed to investigate category information and learn discriminative features.
- Linearized alternating direction method with parallel splitting and adaptive penalty for separable convex programs in machine learning202
This paper proposes LADM with parallel splitting and adaptive penalty (LADMPSAP) to solve multi-block separable convex programs efficiently and proposes a simple optimality measure and reveals the convergence rate of LADmPSAP in an ergodic sense.
- Training High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representation197
The proposed Differentiation on Spike Representation (DSR) method can achieve state-of-the-art SNN performance with low latency on both static and neuromorphic datasets, including CIFar-10, CIFAR-100, ImageNet, and DVS-CIFAR10.
- Convex Sparse Spectral Clustering: Single-View to Multi-View194
This paper proposes the sparse SC (SSC) method that extends the SC with sparse regularization on UUT, and proposes the pairwise SSC that extends SSC to boost the clustering performance by using the multi-view information of data.
- Spatial Pyramid Based Graph Reasoning for Semantic Segmentation193
This paper applies graph convolution into the semantic segmentation task and proposes an improved Laplacian, which gets rid of projecting and re-projecting processes and makes spatial pyramid possible to explore multiple long-range contextual patterns from different scales.
- Neural Collapse Inspired Feature-Classifier Alignment for Few-Shot Class Incremental Learning191
A neural collapse inspired framework for FSCIL that holds the neural collapse optimality and does not break the feature-classifier alignment in an incremental fashion and experiments demonstrate that the proposed framework outperforms the state-of-the-art performances.
- Detecting Doctored JPEG Images Via DCT Coefficient Analysis191
This work proposes an approach that can detect doctored JPEG images and further locate the doctored parts, by examining the double quantization effect hidden among the DCT coefficients.
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- Graph Contrastive Clustering175
This work first proposes a novel graph contrastive learning framework, and then applies it to the clustering task, resulting in the Graph Constrastive Clustering (GCC) method, which incorporates the latent category information to reduce the intra-clusters variance as well as increase the inter-cluster variance.
- Self-Supervised Convolutional Subspace Clustering Network172
An end-to-end trainable framework that combines a dual self-supervision that exploits the output of spectral clustering to supervise the training of the feature learning module and the self-expression module into a joint optimization framework is proposed.
- Bilinear Factor Matrix Norm Minimization for Robust PCA: Algorithms and Applications172
The double nuclear norm and Frobenius/nuclear hybrid norm penalties are defined and it is proved that they are in essence the Schatten-LaTeX quasi-norms, respectively, which lead to much more tractable and scalable Lipschitz optimization problems.
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- Fixed-rank representation for unsupervised visual learning163
It is proved that under some suitable conditions, even with insufficient observations, FRR can still reveal the true subspace memberships, and to achieve robustness to outliers and noise, a sparse regularizer is introduced into the FRR framework.
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- Convolutional Neural Networks with Alternately Updated Clique136
A new convolutional neural network architecture with alternately updated clique (CliqueNet) that effectively avoids the progressive growth of parameters and achieves the state-of-the-art performance with fewer parameters.
- Exact Low Tubal Rank Tensor Recovery from Gaussian Measurements130
It is shown that by solving a TNN minimization problem, the underlying tensor of size n1×n2×n3 with tubal rank r can be exactly recovered when the given number of Gaussian measurements is O(r(n1+n2−r)n3).
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- A comparison of typical ℓ minimization algorithms129
Comp compressed sensing has been widely applied to various areas such as signal processing, machine learning, and pattern recognition and strong incoherence conditions should be imposed.
- Skin Detection -a Short Tutorial119
Several computer vision approaches have been developed for skin detection, which typically transforms a given pixel into an appropriate color space and then uses a skin classifier to label the pixel whether it is a ski n or a non-skin pixel.
- PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation112
A point-wise affinity propagation module based on the Feature Pyramid Network (FPN) framework, named PointFlow, is proposed, which reduces the noise introduced by the background while keeping efficiency.
- Unified Graph and Low-Rank Tensor Learning for Multi-View Clustering110
This paper proposes the unified graph and low-rank tensor learning (UGLTL) for multi-view clustering and unifies the view-specific affinity matrix based on projected graph learning and reorganizes the affinity matrices into tensor form and learns its intrinsic tensor based on low- rank tensor approximation.
- Constructing a Nonnegative Low-Rank and Sparse Graph With Data-Adaptive Features110
Extensive NNLRS experiments demonstrate that the proposed method outperforms the state-of-the-art graph construction method by a large margin for both semisupervised classification and discriminative analysis, which verifies the effectiveness of this proposed method.
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- Emotion Recognition from Arbitrary View Facial Images98
This paper develops a new discriminant analysis theory, aiming at reducing the dimensionality of the facial feature vectors while preserving the most discriminative information, by minimizing an estimated multiclass Bayes error derived under the Gaussian mixture model (GMM).
- A Geometric Analysis of Light Field Rendering98
The key observation is that anti-aliased light field rendering is equivalent to eliminating the “double image” artifacts caused by view interpolation, and a closed-form solution of the minimum sampling rate is presented.
- Towards Memory- and Time-Efficient Backpropagation for Training Spiking Neural Networks96
The Spatial Learning Through Time (SLTT) method is proposed that can achieve high performance while greatly improving training efficiency compared with BPTT, and achieves state-of-the-art accuracy on ImageNet, while the memory cost and training time are reduced by more than 70% and 50%, respectively, compared with BPTT.
- Correntropy Induced L2 Graph for Robust Subspace Clustering95
This paper proposes a new robust clustering method by using the correntropy induced metric, which is robust for handling the non-Gaussian and impulsive noises and extends the method for dealing with the data with outlier rows/features.
- Modeling and rendering of heterogeneous translucent materials using the diffusion equation94
Techniques for modeling and rendering of heterogeneous translucent materials that enable acquisition from measured samples, interactive editing of material attributes, and real-time rendering are proposed.
- Style-preserving English handwriting synthesis85
A novel and effective approach to synthesize English handwriting in the user's writing style, including character glyph, size, slant, and pressure, special connection style, letter spacing, and cursiveness, which is efficiently computed with the aid of a specially designed sample collecting user interface.
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- Robust nuclear norm regularized regression for face recognition with occlusion79
A novel robust nuclear norm regularized regression (RNR) method for face recognition with occlusion, which integrates error detection and error support into one regression model and provides the complexity analysis and convergence analysis of NR.
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- Visual Tuning76
This survey characterizes a large and thoughtful selection of recent works, providing a systematic and comprehensive overview of existing work and models, and categorizes recent visual tuning techniques into five groups: fine-tuning, prompt tuning, adapter tuning, parameter tuning, and remapping tuning.
- On the Convergence of Learning-Based Iterative Methods for Nonconvex Inverse Problems76
The theoretical analysis reveals that FIMA allows us to generate globally convergent trajectories for learning-based iterative methods, and the devised scheduling policies on flexible modules should also be beneficial for classical numerical methods in the nonconvex scenario.
- Robust latent low rank representation for subspace clustering75
This work proposes choosing the sparest solution in the solution set when there is noise, and proposes preprocessing the data with robust PCA to demonstrate the advantage of the robust LatLRR over state-of-the-art methods.
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- SOGNet: Scene Overlap Graph Network for Panoptic Segmentation65
This study aims to model overlap relations among instances and resolve them for panoptic segmentation, Inspired by scene graph representation, and forms the overlapping problem as a simplified case, named scene overlap graph.
- Optimized projections for compressed sensing via direct mutual coherence minimization65
To the best of the knowledge, this is the first work which directly minimizes the mutual coherence of the projected dictionary with a convergence guarantee and can recover sparse signals better than existing methods.
- A Unified Convex Surrogate for the Schatten-p Norm65
To utilize the convexity of the factor matrix norms, the accelerated proximal alternating linearized minimization algorithm is adopted and its superior performance over the state-of-the-art methods is exhibited.
- Linearized Alternating Direction Method with Adaptive Penalty and Warm Starts for Fast Solving Transform Invariant Low-Rank Textures64
This paper proposes a novel algorithm to speed up solving TILT, with guaranteed convergence for the inner loop, based on the recently proposed linearized alternating direction method with adaptive penalty.
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- Tensor Q-rank: new data dependent definition of tensor rank60
A new definition of data dependent tensor rank named tensor Q-rank is proposed by a learnable orthogonal matrix Q and two explainable selection methods of Q are introduced, under which the data tensor may have a more significant low tensorQ-rank structure than that of low tubal- rank structure.
- Two-weight and three-weight linear codes based on Weil sums60
Several classes of two-weight and three-weight linear codes are presented and their weight distributions are determined using Weil sums and some of the linear codes obtained are optimal or almost optimal with respect to the Griesmer bound.
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- Learning PDEs for Image Restoration via Optimal Control57
The design of PDEs could be made easier by borrowing the learning strategy from machine learning, and the effectiveness of the learning-based PDE (L-PDE) framework for image restoration is demonstrated.
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- Towards Improving the Consistency, Efficiency, and Flexibility of Differentiable Neural Architecture Search52
This paper introduces EnTranNAS that is composed of Engine-cells and Transit-cells, and develops an architecture derivation method to replace the traditional one that is based on a hand-crafted rule, which enables differentiable sparsification, and keeps the derived architecture equivalent to that of engines, which further improves the consistency between search and evaluation.
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- Accelerated Alternating Direction Method of Multipliers: An Optimal O(1 / K) Nonergodic Analysis50
The accelerated ADMM proposed in Ouyang et al. is modified and an O(1 / K) nonergodic convergence rate analysis is given, which satisfies |F(xK)-F(x∗)|≤O(1/K)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb}
- Revisiting EXTRA for Smooth Distributed Optimization49
A sharp complexity analysis for EXTRA with the improved improved Catalyst framework is given and the strong convexity is absent and communication complexities of the accelerated EXTRA are only worse by the factors.
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- Invertible Rescaling Network and Its Extensions46
A novel invertible framework is proposed to handle the bidirectional degradation and restoration from a new perspective, i.e. invertable bijective transformation, to model the information loss of pre-degradation in the form of distribution, which could mitigate the ill-posed problem during post-restoration.
- Multi-output regression on the output manifold46
This paper considers the output space as a Riemannian submanifold to incorporate its geometric structure into the regression process and proposes a novel mechanism, called locally linear transformation (LLT), to define the loss functions on the output manifold.
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- Cross-patient automatic epileptic seizure detection using patient-adversarial neural networks with spatio-temporal EEG augmentation45
Two aspects for solving the cross-patient generalization problem in deep learning approaches are considered, including a new data augmentation method to largely improve generalization and a proposed patient-adversarial neural network to learn a patient-invariant representation for better generalization.
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- Joint Latent Subspace Learning and Regression for Cross-Modal Retrieval41
This paper first adopts the spectral regression method to learn the optimal latent space shared by data of all modalities based on the orthogonal constraints, then builds a graph model to project the multi-modality data into the latent space.
- Multiple Models Fusion for Emotion Recognition in the Wild37
This paper proposes a multiple models fusion method to automatically recognize the expression in the video clip as part of the third Emotion Recognition in the Wild Challenge (EmotiW 2015).
- Deep learning‐based facial analysis for predicting difficult videolaryngoscopy: a feasibility study36
Artificial intelligence‐based facial analysis is a feasible technique for predicting difficulty during videolaryngoscopy, and the model developed using neural networks has higher predictive performance than traditional methods.
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- A Survey on Video Temporal Grounding With Multimodal Large Language Model34
Current research on VTG-MLLMs is examined through a three-dimensional taxonomy: the functional roles of MLLMs, highlighting their architectural significance; training paradigms, analyzing strategies for temporal reasoning and task adaptation; and video feature processing techniques, which determine spatiotemporal representation effectiveness.
- Feature learning via partial differential equation with applications to face recognition34
The experimental results of face recognition on the four benchmark face datasets show that the proposed method outperforms the state-of-the-art feature learning methods in the case of low-resolution images and when the training samples are limited.
- Locality-preserving low-rank representation for graph construction from nonlinear manifolds33
This paper proposes a novel method to learn an undirected graph from a mixture of nonlinear manifolds via Locality-Preserving Low-Rank Representation (L 2 R 2), which extents the original LRR model from linear subspaces to non linear manifolds.
- Efficient and generalizable cross-patient epileptic seizure detection through a spiking neural network32
An EEG-based spiking neural network (EESNN) with a recurrent spiking convolution structure, which may better take advantage of temporal and biological characteristics in EEG signals and can achieve energy reduction by several orders of magnitude compared with ANNs according to the theoretical estimation.
- Tensor linear Laplacian discrimination (TLLD) for feature extraction32
Experimental results on face recognition, texture classification and handwritten digit recognition show that the tensor linear Laplacian discrimination algorithm TLLD is effective in extracting discriminative features.
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- Panoptic-PartFormer++: A Unified and Decoupled View for Panoptic Part Segmentation29
The first end-to-end unified framework, Panoptic-PartFormer is designed, designing a meta-architecture that decouples part features and things/stuff features, respectively and proposes a new metric Part-Whole Quality (PWQ), better to measure this task from pixel-region and part-whole perspectives.
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- Locality-constrained linear coding based bi-layer model for multi-view facial expression recognition29
This paper proposes a simple yet effective method, called the locality-constrained linear coding based bi-layer (LLCBL) model, to learn discriminative representation for multi-view facial expression recognition that outperforms the state-of-the-art methods.
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- Dictionary learning with structured noise27
A novel dictionary learning with structured noise (DLSN) method for handling noisy data that learns an adaptive dictionary to characterize structured noise and employs the l 2 norm to depict Gaussian noise.
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- Dynamical System Inspired Adaptive Time Stepping Controller for Residual Network Families24
An adaptive time stepping controller is developed that is dependent on the parameters of the current step, and aware of previous steps, and jointly optimized with the network training so that variable step sizes and evolution time can be adaptively adjusted.
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- Proximal Iteratively Reweighted Algorithm with Multiple Splitting for Nonconvex Sparsity Optimization23
This paper proposes the Proximal Iteratively REweighted (PIRE) algorithm for solving a general problem, which involves a large body of nonconvex sparse and structured sparse related problems and proves that the proposed methods converge and any limit solution is a stationary point.
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- Efficient tree-structured SfM by RANSAC generalized Procrustes analysis22
A tree-structured structure-from-motion (SfM) method that recovers 3D scene structures and estimates camera poses from unordered image sets and is able to produce metric reconstructions efficiently and robustly is proposed.
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- Neural Collapse Terminus: A Unified Solution for Class Incremental Learning and Its Variants18
A unified solution to the misalignment dilemma in the three tasks of CIL, LTCIL and FSCIL is offered andoretical analysis indicates that the method holds the neural collapse optimality in an incremental fashion regardless of data imbalance or data scarcity.
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- A Roadmap for Big Model17
This paper discusses not only the BM technologies themselves but also the prerequisites for BM training and applications with BMs, dividing the BM review into four parts: Resource, Models, Key Technologies and Application.
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- Multi-view common space learning for emotion recognition in the wild16
This paper proposes a method to analyse multi-view features of emotion samples and automatically recognize the expression as part of the fourth Emotion Recognition in the Wild Challenge (EmotiW 2016).
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- R$$^{2}$$-Net: Recurrent and Recursive Network for Sparse-View CT Artifacts Removal13
A novel neural network architecture to reduce streak artifacts generated in sparse-view 2D Computed Tomography image reconstruction is proposed, which outperforms the state-of-the-art methods both quantitatively and qualitatively.
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- ShadowNet for Data-Centric Quantum System Learning10
This work presents ShadowNet, implemented under both convolutional and attention mechanisms, to efficiently and faithfully tackle two pivotal QSL tasks: quantum state tomography (QST) and direct fidelity estimation (DFE).
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- PDO-s3DCNNs: Partial Differential Operator Based Steerable 3D CNNs9
PDO-s3DCNNs are the most general steerable CNNs for 3D rotations, in the sense that they cover all common subgroups of SO (3) and their representations, while existing methods can only be applied to specific groups and representations.
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- Joint Dictionary Learning and Semantic Constrained Latent Subspace Projection for Cross-Modal Retrieval9
A novel joint dictionary learning and semantic constrained latent subspace learning method for cross-modal retrieval~(JDSLC) to deal with above two issues and outperforms the state-of-the-art approaches.
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- SSCNet: learning-based subspace clustering4
This work redefine and generalize the optimization procedure of the linearized alternating direction method of multipliers (ADMM), framing it as a multi-block deep neural network, where each block corresponds to a linearized ADMM iteration step, to address the SSC problem.
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- Newton design: designing CNNs with the family of Newton’s methods4
A unified framework for understanding and designing CNN architectures with the family of Newton’s methods, which is referred to as Newton design is proposed, and it is observed that the standard feedforward CNN model (PlainNet) solves an optimization problem via a kind of quasi-Newton method.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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