Third International Workshop on Robust Subspace Learning and Applications in Computer Vision Call for Papers

Call for papers


Third International Workshop RSL-CV 2019, Seoul, Korea, October 27, 2019.

Third  International Workshop on Robust Subspace Learning and Applications in Computer Vision
in conjunction with ICCV 2019

Robust subspace learning/tracking/clustering either based on robust
statistics estimation on reconstruction error and on decompositions
into low-rank/sparse plus additive matrices/tensors provide suitable
frameworks for many computer vision applications like in video coding,
key frame extraction, hyper-spectral video processing, dynamic MRI,
motion saliency detection, background initialization and
background/foreground separation. In this context, the previous
workshops RSL-CV hosted at ICCV 2015 and ICCV 2017 aimed to propose
novel robust subspace clustering/learning/tracking approaches with
adaptive and incremental algorithms.

Even if progress have been made since the last decade, there are still
main challenges that concern the fundamental design of relaxed models
and solvers that have to be with iterations as few as possible, and as
efficient as possible. In addition, efforts should concentrated on
provable correct algorithms with convergence guarantees as well as
robust subspace recovery algorithms. Furthermore, recent advances on
low-rank and sparse embedding for dimensionality reduction, robust
graph learning and robust deep auto-encoders give promising gap of
performance by applying them in computer vision. Finally, even if many
efforts have been made to develop methods that perform well visually
with reduced computational cost, no algorithm has emerged that is able
to simultaneously address all of the key challenges that accompany
real world videos taken by static or moving cameras like illumination
changes, dynamic backgrounds, bootstrapping that generate corrupted
and missing data.

The goals of this workshop are thus three-fold: 1) designing robust
subspace methods for computer vision applications; 2) proposing new
adaptive and incremental algorithms with convergence guarantees that
reach the requirements of real-time applications (motion saliency,
video coding and background/foreground separation); and 3) proposing
robust algorithms to handle the key challenges in computer vision

Papers are solicited to address robust subspace
clustering/learning/tracking based on matrix/tensor decomposition, to
be applied in computer vision, including but not limited to the


Robust Subspace Learning: RPCA, RMF, RMC

Robust Low Rank Factorization


Robust Subspace Tracking

Robust Subspace Clustering

Decomposition into low-rank/sparse plus additive


Bayesian RPCA, Fuzzy RPCA

Compressive Sensing

Dictionary Learning

Structured Sparsity, Dynamic Group Sparsity

Solvers (ALM, ADM, etc…),

Closed form solutions

Efficient SVD algorithms

Multilevel RPCA

Incremental RPCA

Real time implementation on GPU

Embedded implementation

Deep Learning

Robust Deep Auto-Encoders

We encourage authors to evaluate their approach on at least one of the
reference datasets for each application
(Please see the Computer Vision Datasets).