Fourth Workshop on Robust Subspace Learning and Computer Vision Call for Papers
Fourth Workshop on Robust Subspace Learning and Computer Vision
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, ICCV 2017 and ICCV 2019 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
Structured Sparsity, Dynamic Group Sparsity
Solvers (ALM, ADM, etc…),
Closed form solutions
Efficient SVD algorithms
Real time implementation on GPU
Robust Deep Auto-Encoders
Full Paper Submission Deadline:
July 13, 2021 (for papers not submitted at ICCV)
July 25, 2021 (for papers that are awaiting for ICCV decisions)
Decisions to Authors:
July 31, 2021
August 14, 2021
Thierry Bouwmans, Associate Professor, Laboratoire MIA, Univ. La Rochelle, France.
Soon Ki Jung, Professor, Kyungpook National University, Korea.
Panos Markopoulos , Associate Professor, Rochester Institute of Technology, USA.
Paul Rodriguez, Full Professor, DSP / DIP Laboratory, Pontificia Universidad Católica del Perú, Peru.
Mohamed Shehata, Associate Professor, Memorial University, Canada.
René Vidal, Full Professor, Johns Hopkins University , USA.
El-Hadi Zahzah , Associate Professor, Laboratoire L3I, Univ. La Rochelle, France