5,757 research outputs found
Unsupervised learning of generative topic saliency for person re-identification
(c) 2014. The copyright of this document resides with its authors.
It may be distributed unchanged freely in print or electronic forms.© 2014. The copyright of this document resides with its authors. Existing approaches to person re-identification (re-id) are dominated by supervised learning based methods which focus on learning optimal similarity distance metrics. However, supervised learning based models require a large number of manually labelled pairs of person images across every pair of camera views. This thus limits their ability to scale to large camera networks. To overcome this problem, this paper proposes a novel unsupervised re-id modelling approach by exploring generative probabilistic topic modelling. Given abundant unlabelled data, our topic model learns to simultaneously both (1) discover localised person foreground appearance saliency (salient image patches) that are more informative for re-id matching, and (2) remove busy background clutters surrounding a person. Extensive experiments are carried out to demonstrate that the proposed model outperforms existing unsupervised learning re-id methods with significantly simplified model complexity. In the meantime, it still retains comparable re-id accuracy when compared to the state-of-the-art supervised re-id methods but without any need for pair-wise labelled training data
Semantic Autoencoder for Zero-Shot Learning
Existing zero-shot learning (ZSL) models typically learn a projection
function from a feature space to a semantic embedding space (e.g.~attribute
space). However, such a projection function is only concerned with predicting
the training seen class semantic representation (e.g.~attribute prediction) or
classification. When applied to test data, which in the context of ZSL contains
different (unseen) classes without training data, a ZSL model typically suffers
from the project domain shift problem. In this work, we present a novel
solution to ZSL based on learning a Semantic AutoEncoder (SAE). Taking the
encoder-decoder paradigm, an encoder aims to project a visual feature vector
into the semantic space as in the existing ZSL models. However, the decoder
exerts an additional constraint, that is, the projection/code must be able to
reconstruct the original visual feature. We show that with this additional
reconstruction constraint, the learned projection function from the seen
classes is able to generalise better to the new unseen classes. Importantly,
the encoder and decoder are linear and symmetric which enable us to develop an
extremely efficient learning algorithm. Extensive experiments on six benchmark
datasets demonstrate that the proposed SAE outperforms significantly the
existing ZSL models with the additional benefit of lower computational cost.
Furthermore, when the SAE is applied to supervised clustering problem, it also
beats the state-of-the-art.Comment: accepted to CVPR201
The silicate model and carbon rich model of CoRoT-7b, Kepler-9d and Kepler-10b
Possible bulk compositions of the super-Earth exoplanets, CoRoT-7b,
Kepler-9d, and Kepler-10b are investigated by applying a commonly used silicate
and a non-standard carbon model. Their internal structures are deduced using
the suitable equation of state of the materials. The degeneracy problems of
their compositions can be partly overcome, based on the fact that all three
planets are extremely close to their host stars. By analyzing the numerical
results, we conclude: 1) The iron core of CoRoT-7b is not more than 27% of its
total mass within 1 mass-radius error bars, so an Earth-like
composition is less likely, but its carbon rich model can be compatible with an
Earth-like core/mantle mass fraction; 2) Kepler-10b is more likely with a
Mercury-like composition, its old age implies that its high iron content may be
a result of strong solar wind or giant impact; 3) the transiting-only
super-Earth Kepler-9d is also discussed. Combining its possible composition
with the formation theory, we can place some constraints on its mass and bulk
composition.Comment: 20 pages, 8figures, accepted for publication in RAA. arXiv admin
note: text overlap with arXiv:0707.289
Control the high-order harmonics cutoff through the combination of chirped laser and static electric field
The high harmonic generation from atoms in the combination of chirped laser
pulse and static field is theoretically investigated. For the first time, we
explore a further physical mechanism of the significant extension of high
harmonic generation cutoff based on three-step model. It is shown that the
cutoff is substantially extended due to the asymmetry of the combined field. If
appropriate parameters are chosen, the cutoff of high harmonic generation can
reach Ip+42Up. Furthermore, an ultrabroad super-continuum spectrum can be
generated. When the phases are properly compensated for, an isolated 9
attosecond pulse can be obtained.Comment: 7 pages 5figure
Highly Efficient Regression for Scalable Person Re-Identification
Existing person re-identification models are poor for scaling up to large
data required in real-world applications due to: (1) Complexity: They employ
complex models for optimal performance resulting in high computational cost for
training at a large scale; (2) Inadaptability: Once trained, they are
unsuitable for incremental update to incorporate any new data available. This
work proposes a truly scalable solution to re-id by addressing both problems.
Specifically, a Highly Efficient Regression (HER) model is formulated by
embedding the Fisher's criterion to a ridge regression model for very fast
re-id model learning with scalable memory/storage usage. Importantly, this new
HER model supports faster than real-time incremental model updates therefore
making real-time active learning feasible in re-id with human-in-the-loop.
Extensive experiments show that such a simple and fast model not only
outperforms notably the state-of-the-art re-id methods, but also is more
scalable to large data with additional benefits to active learning for reducing
human labelling effort in re-id deployment
Semantic Graph for Zero-Shot Learning
Zero-shot learning aims to classify visual objects without any training data
via knowledge transfer between seen and unseen classes. This is typically
achieved by exploring a semantic embedding space where the seen and unseen
classes can be related. Previous works differ in what embedding space is used
and how different classes and a test image can be related. In this paper, we
utilize the annotation-free semantic word space for the former and focus on
solving the latter issue of modeling relatedness. Specifically, in contrast to
previous work which ignores the semantic relationships between seen classes and
focus merely on those between seen and unseen classes, in this paper a novel
approach based on a semantic graph is proposed to represent the relationships
between all the seen and unseen class in a semantic word space. Based on this
semantic graph, we design a special absorbing Markov chain process, in which
each unseen class is viewed as an absorbing state. After incorporating one test
image into the semantic graph, the absorbing probabilities from the test data
to each unseen class can be effectively computed; and zero-shot classification
can be achieved by finding the class label with the highest absorbing
probability. The proposed model has a closed-form solution which is linear with
respect to the number of test images. We demonstrate the effectiveness and
computational efficiency of the proposed method over the state-of-the-arts on
the AwA (animals with attributes) dataset.Comment: 9 pages, 5 figure
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