15 research outputs found
On MMSE and MAP Denoising Under Sparse Representation Modeling Over a Unitary Dictionary
Among the many ways to model signals, a recent approach that draws
considerable attention is sparse representation modeling. In this model, the
signal is assumed to be generated as a random linear combination of a few atoms
from a pre-specified dictionary. In this work we analyze two Bayesian denoising
algorithms -- the Maximum-Aposteriori Probability (MAP) and the
Minimum-Mean-Squared-Error (MMSE) estimators, under the assumption that the
dictionary is unitary. It is well known that both these estimators lead to a
scalar shrinkage on the transformed coefficients, albeit with a different
response curve. In this work we start by deriving closed-form expressions for
these shrinkage curves and then analyze their performance. Upper bounds on the
MAP and the MMSE estimation errors are derived. We tie these to the error
obtained by a so-called oracle estimator, where the support is given,
establishing a worst-case gain-factor between the MAP/MMSE estimation errors
and the oracle's performance. These denoising algorithms are demonstrated on
synthetic signals and on true data (images).Comment: 29 pages, 10 figure
Compact Network Training for Person ReID
The task of person re-identification (ReID) has attracted growing attention
in recent years leading to improved performance, albeit with little focus on
real-world applications. Most SotA methods are based on heavy pre-trained
models, e.g. ResNet50 (~25M parameters), which makes them less practical and
more tedious to explore architecture modifications. In this study, we focus on
a small-sized randomly initialized model that enables us to easily introduce
architecture and training modifications suitable for person ReID. The outcomes
of our study are a compact network and a fitting training regime. We show the
robustness of the network by outperforming the SotA on both Market1501 and
DukeMTMC. Furthermore, we show the representation power of our ReID network via
SotA results on a different task of multi-object tracking