49,051 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
Generation of Multi-Color Attosecond X-Ray Radiation Through Modulation Compression
In this paper, we propose a scheme to generate tunable multi-color attosecond
coherent X-ray radiation for future light source applications. This scheme uses
an energy chirped electron beam, a laser modulators, a laser chirper and two
bunch compressors to generate a multi-spike prebunched kilo-Ampere current
electron beam from a few tens Ampere electron beam out of a linac. Such an
electron beam transports through a series of undulator radiators and bunch
compressors to generate multi-color coherent X-ray radiation. As an
illustration, we present an example to generate two attosecond pulses with
nm and nm coherent X-ray radiation wavelength and more than MW
peak power using a Ampere nm laser seeded electron beam
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
Denoising Deep Neural Networks Based Voice Activity Detection
Recently, the deep-belief-networks (DBN) based voice activity detection (VAD)
has been proposed. It is powerful in fusing the advantages of multiple
features, and achieves the state-of-the-art performance. However, the deep
layers of the DBN-based VAD do not show an apparent superiority to the
shallower layers. In this paper, we propose a denoising-deep-neural-network
(DDNN) based VAD to address the aforementioned problem. Specifically, we
pre-train a deep neural network in a special unsupervised denoising greedy
layer-wise mode, and then fine-tune the whole network in a supervised way by
the common back-propagation algorithm. In the pre-training phase, we take the
noisy speech signals as the visible layer and try to extract a new feature that
minimizes the reconstruction cross-entropy loss between the noisy speech
signals and its corresponding clean speech signals. Experimental results show
that the proposed DDNN-based VAD not only outperforms the DBN-based VAD but
also shows an apparent performance improvement of the deep layers over
shallower layers.Comment: This paper has been accepted by IEEE ICASSP-2013, and will be
published online after May, 201
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