3,427 research outputs found
Constrained Deep Transfer Feature Learning and its Applications
Feature learning with deep models has achieved impressive results for both
data representation and classification for various vision tasks. Deep feature
learning, however, typically requires a large amount of training data, which
may not be feasible for some application domains. Transfer learning can be one
of the approaches to alleviate this problem by transferring data from data-rich
source domain to data-scarce target domain. Existing transfer learning methods
typically perform one-shot transfer learning and often ignore the specific
properties that the transferred data must satisfy. To address these issues, we
introduce a constrained deep transfer feature learning method to perform
simultaneous transfer learning and feature learning by performing transfer
learning in a progressively improving feature space iteratively in order to
better narrow the gap between the target domain and the source domain for
effective transfer of the data from the source domain to target domain.
Furthermore, we propose to exploit the target domain knowledge and incorporate
such prior knowledge as a constraint during transfer learning to ensure that
the transferred data satisfies certain properties of the target domain. To
demonstrate the effectiveness of the proposed constrained deep transfer feature
learning method, we apply it to thermal feature learning for eye detection by
transferring from the visible domain. We also applied the proposed method for
cross-view facial expression recognition as a second application. The
experimental results demonstrate the effectiveness of the proposed method for
both applications.Comment: International Conference on Computer Vision and Pattern Recognition,
201
Constrained Joint Cascade Regression Framework for Simultaneous Facial Action Unit Recognition and Facial Landmark Detection
Cascade regression framework has been shown to be effective for facial
landmark detection. It starts from an initial face shape and gradually predicts
the face shape update from the local appearance features to generate the facial
landmark locations in the next iteration until convergence. In this paper, we
improve upon the cascade regression framework and propose the Constrained Joint
Cascade Regression Framework (CJCRF) for simultaneous facial action unit
recognition and facial landmark detection, which are two related face analysis
tasks, but are seldomly exploited together. In particular, we first learn the
relationships among facial action units and face shapes as a constraint. Then,
in the proposed constrained joint cascade regression framework, with the help
from the constraint, we iteratively update the facial landmark locations and
the action unit activation probabilities until convergence. Experimental
results demonstrate that the intertwined relationships of facial action units
and face shapes boost the performances of both facial action unit recognition
and facial landmark detection. The experimental results also demonstrate the
effectiveness of the proposed method comparing to the state-of-the-art works.Comment: International Conference on Computer Vision and Pattern Recognition,
201
Light speed variation from gamma ray bursts: criteria for low energy photons
We examine a method to detect the light speed variation from gamma ray burst
data observed by the Fermi Gamma-ray Space Telescope (FGST). We suggest new
criteria to determine the characteristic time for low energy photons by the
energy curve and the average energy curve, and obtain similar results compared
with those from the light curve. We offer a new criterion with both the light
curve and the average energy curve to determine the characteristic time for low
energy photons. We then apply the new criteria to the GBM NaI data, the GBM BGO
data, and the LAT LLE data, and obtain consistent results for three different
sets of low energy photons from different FERMI detectors.Comment: 26 latex pages, 23 figures, final version for publicatio
Simultaneous Facial Landmark Detection, Pose and Deformation Estimation under Facial Occlusion
Facial landmark detection, head pose estimation, and facial deformation
analysis are typical facial behavior analysis tasks in computer vision. The
existing methods usually perform each task independently and sequentially,
ignoring their interactions. To tackle this problem, we propose a unified
framework for simultaneous facial landmark detection, head pose estimation, and
facial deformation analysis, and the proposed model is robust to facial
occlusion. Following a cascade procedure augmented with model-based head pose
estimation, we iteratively update the facial landmark locations, facial
occlusion, head pose and facial de- formation until convergence. The
experimental results on benchmark databases demonstrate the effectiveness of
the proposed method for simultaneous facial landmark detection, head pose and
facial deformation estimation, even if the images are under facial occlusion.Comment: International Conference on Computer Vision and Pattern Recognition,
201
Invertible condition of quantum Fisher information matrix for a mixed qubit
Estimating multiparamter simultaneously as precise as possible is an
important goal of quantum metrolo- gy. As a first step to this end, here we
give a condition determining whether two arbitrary parameters can be estimated
simultaneously for a qubit in the mixed state. An application of this condition
is shown.Comment: The European Physical Journal D 201
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