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Found 21 papers, 2 papers with code
Dual-Frequency Filtering Self-aware Graph Neural Networks for Homophilic and Heterophilic Graphs
Graph Neural Networks (GNNs) have excelled in handling graph-structured data, attracting significant research interest.
Hierarchical Multi-modal Transformer for Cross-modal Long Document Classification
In this paper, we propose a novel approach called Hierarchical Multi-modal Transformer (HMT) for cross-modal long document classification.
DGNN: Decoupled Graph Neural Networks with Structural Consistency between Attribute and Graph Embedding Representations
To obtain a more comprehensive embedding representation of nodes, a novel GNNs framework, dubbed Decoupled Graph Neural Networks (DGNN), is introduced.
Adversarial Privacy-preserving Filter
While widely adopted in practical applications, face recognition has been critically discussed regarding the malicious use of face images and the potential privacy problems, e. g., deceiving payment system and causing personal sabotage.
blessing in disguise: Designing Robust Turing Test by Employing Algorithm Unrobustness
Turing test was originally proposed to examine whether machine's behavior is indistinguishable from a human.
Vectorial Dimension Reduction for Tensors Based on Bayesian Inference
Under this expression, the projection base of the model is based on the tensor CandeComp/PARAFAC (CP) decomposition and the number of free parameters in the model only grows linearly with the number of modes rather than exponentially.
Localized LRR on Grassmann Manifolds: An Extrinsic View
Subspace data representation has recently become a common practice in many computer vision tasks.
Locality Preserving Projections for Grassmann manifold
Learning on Grassmann manifold has become popular in many computer vision tasks, with the strong capability to extract discriminative information for imagesets and videos.
Partial Least Squares Regression on Riemannian Manifolds and Its Application in Classifications
Partial least squares regression (PLSR) has been a popular technique to explore the linear relationship between two datasets.
Matrix Variate RBM Model with Gaussian Distributions
Restricted Boltzmann Machine (RBM) is a particular type of random neural network models modeling vector data based on the assumption of Bernoulli distribution.
Laplacian LRR on Product Grassmann Manifolds for Human Activity Clustering in Multi-Camera Video Surveillance
In multi-camera video surveillance, it is challenging to represent videos from different cameras properly and fuse them efficiently for specific applications such as human activity recognition and clustering.
Partial Sum Minimization of Singular Values Representation on Grassmann Manifolds
As a significant subspace clustering method, low rank representation (LRR) has attracted great attention in recent years.
Kernelized LRR on Grassmann Manifolds for Subspace Clustering
The novelty of this paper is to generalize LRR on Euclidean space onto an LRR model on Grassmann manifold in a uniform kernelized LRR framework.
Mixture of Bilateral-Projection Two-dimensional Probabilistic Principal Component Analysis
This paper proposes a mixture of bilateral-projection probabilistic principal component analysis model (mixB2DPPCA) on 2D data.
Block-Diagonal Sparse Representation by Learning a Linear Combination Dictionary for Recognition
In a sparse representation based recognition scheme, it is critical to learn a desired dictionary, aiming both good representational power and discriminative performance.
Matrix Variate RBM and Its Applications
In this paper, a Matrix-Variate Restricted Boltzmann Machine (MVRBM) model is proposed by generalizing the classic RBM to explicitly model matrix data.
Tensor Sparse and Low-Rank based Submodule Clustering Method for Multi-way Data
A new submodule clustering method via sparse and low-rank representation for multi-way data is proposed in this paper.
Fast Optimization Algorithm on Riemannian Manifolds and Its Application in Low-Rank Representation
The paper addresses the problem of optimizing a class of composite functions on Riemannian manifolds and a new first order optimization algorithm (FOA) with a fast convergence rate is proposed.
Low Rank Representation on Grassmann Manifolds: An Extrinsic Perspective
Many computer vision algorithms employ subspace models to represent data.
Kernelized Low Rank Representation on Grassmann Manifolds
One of its successful applications is subspace clustering which means data are clustered according to the subspaces they belong to.
Heterogeneous Tensor Decomposition for Clustering via Manifold Optimization
In contrast to existing techniques, we propose a new clustering algorithm that alternates between different modes of the proposed heterogeneous tensor model.
