RSL-CV15
* *Robust Subspace Learning and Applications in Computer Vision
* Adaptive Low Rank Approximation for Tensors
* Background Subtraction via Superpixel-Based Online Matrix Decomposition with Structured Foreground Constraints
* Dual Principal Component Pursuit
* Filtrated Spectral Algebraic Subspace Clustering
* Image Saliency Detection with Sparse Representation of Learnt Texture Atoms
* Multi-resolution Dynamic Mode Decomposition for Foreground/Background Separation and Object Tracking
* Object Extraction from Bounding Box Prior with Double Sparse Reconstruction
* Online Stochastic Tensor Decomposition for Background Subtraction in Multispectral Video Sequences
* Pose and Expression-Coherent Face Recovery in the Wild
* Robust Matrix Regression for Illumination and Occlusion Tolerant Face Recognition
* Simple Method for Subspace Estimation with Corrupted Columns, A
* Sparse Subspace Clustering for Incomplete Images
* Visual Tracking via Nonnegative Regularization Multiple Locality Coding
14 for RSL-CV15
RSL-CV17
* *Robust Subspace Learning and Applications in Computer Vision
* Background Subtraction via Fast Robust Matrix Completion
* Batch-Incremental Video Background Estimation Model Using Weighted Low-Rank Approximation of Matrices, A
* Compressed Singular Value Decomposition for Image and Video Processing
* Dynamic Mode Decomposition for Background Modeling
* Fast Approximate Karhunen-Loeve Transform for Three-Way Array Data
* Learning Robust Representations for Computer Vision
* Manifold Constrained Low-Rank Decomposition
* Non-convex Relaxation for Fixed-Rank Approximation, A
* Panning and Jitter Invariant Incremental Principal Component Pursuit for Video Background Modeling
* Robust and Scalable Column/Row Sampling from Corrupted Big Data
* UHD Video Super-Resolution Using Low-Rank and Sparse Decomposition
* Variational Robust Subspace Clustering with Mean Update Algorithm
* Weighted Low Rank Approximation for Background Estimation Problems
14 for RSL-CV17
RSL-CV19
* *Robust Subspace Learning and Applications in Computer Vision
* Adaptive Online k-Subspaces with Cooperative Re-Initialization
* Classifying and Comparing Approaches to Subspace Clustering with Missing Data
* Complete Moving Object Detection in the Context of Robust Subspace Learning
* Deep Closed-Form Subspace Clustering
* Learning Disentangled Representations via Independent Subspaces
* Low-Rank Tensor Tracking
* Panoramic Video Separation with Online Grassmannian Robust Subspace Estimation
* Robust Discrimination and Generation of Faces using Compact, Disentangled Embeddings
* Structure-Constrained Feature Extraction by Autoencoders for Subspace Clustering
* Structuring Autoencoders
* Tensor Subspace Learning and Classification: Tensor Local Discriminant Embedding for Hyperspectral Image
* Tensor Train Decomposition for Efficient Memory Saving in Perceptual Feature-Maps
* Topological Labelling of Scene using Background/Foreground Separation and Epipolar Geometry
* Uncalibrated Non-Rigid Factorisation by Independent Subspace Analysis
15 for RSL-CV19