20,755 research outputs found
Object Detection in 20 Years: A Survey
Object detection, as of one the most fundamental and challenging problems in
computer vision, has received great attention in recent years. Its development
in the past two decades can be regarded as an epitome of computer vision
history. If we think of today's object detection as a technical aesthetics
under the power of deep learning, then turning back the clock 20 years we would
witness the wisdom of cold weapon era. This paper extensively reviews 400+
papers of object detection in the light of its technical evolution, spanning
over a quarter-century's time (from the 1990s to 2019). A number of topics have
been covered in this paper, including the milestone detectors in history,
detection datasets, metrics, fundamental building blocks of the detection
system, speed up techniques, and the recent state of the art detection methods.
This paper also reviews some important detection applications, such as
pedestrian detection, face detection, text detection, etc, and makes an in-deep
analysis of their challenges as well as technical improvements in recent years.Comment: This work has been submitted to the IEEE TPAMI for possible
publicatio
Nonparametric Detection of Anomalous Data Streams
A nonparametric anomalous hypothesis testing problem is investigated, in
which there are totally n sequences with s anomalous sequences to be detected.
Each typical sequence contains m independent and identically distributed
(i.i.d.) samples drawn from a distribution p, whereas each anomalous sequence
contains m i.i.d. samples drawn from a distribution q that is distinct from p.
The distributions p and q are assumed to be unknown in advance.
Distribution-free tests are constructed using maximum mean discrepancy as the
metric, which is based on mean embeddings of distributions into a reproducing
kernel Hilbert space. The probability of error is bounded as a function of the
sample size m, the number s of anomalous sequences and the number n of
sequences. It is then shown that with s known, the constructed test is
exponentially consistent if m is greater than a constant factor of log n, for
any p and q, whereas with s unknown, m should has an order strictly greater
than log n. Furthermore, it is shown that no test can be consistent for
arbitrary p and q if m is less than a constant factor of log n, thus the
order-level optimality of the proposed test is established. Numerical results
are provided to demonstrate that our tests outperform (or perform as well as)
the tests based on other competitive approaches under various cases.Comment: Submitted to IEEE Transactions on Signal Processing, 201
The meson-exchange model for the interaction
In the present work, we apply the one-boson-exchange potential (OBEP) model
to investigate the possibility of Y(2175) and as bound states of
and respectively.
We consider the effective potential from the pseudoscalar -exchange and
-exchange, the scalar -exchange, and the vector
-exchange and -exchange. The and meson exchange
potential is repulsive force for the state and attractive for .
The results depend very sensitively on the cutoff parameter of the
-exchange () and least sensitively on that of the
-exchange (). Our result suggests the possible
interpretation of Y(2175) and as the bound states of
and respectively
ODN: Opening the Deep Network for Open-set Action Recognition
In recent years, the performance of action recognition has been significantly
improved with the help of deep neural networks. Most of the existing action
recognition works hold the \textit{closed-set} assumption that all action
categories are known beforehand while deep networks can be well trained for
these categories. However, action recognition in the real world is essentially
an \textit{open-set} problem, namely, it is impossible to know all action
categories beforehand and consequently infeasible to prepare sufficient
training samples for those emerging categories. In this case, applying
closed-set recognition methods will definitely lead to unseen-category errors.
To address this challenge, we propose the Open Deep Network (ODN) for the
open-set action recognition task. Technologically, ODN detects new categories
by applying a multi-class triplet thresholding method, and then dynamically
reconstructs the classification layer and "opens" the deep network by adding
predictors for new categories continually. In order to transfer the learned
knowledge to the new category, two novel methods, Emphasis Initialization and
Allometry Training, are adopted to initialize and incrementally train the new
predictor so that only few samples are needed to fine-tune the model. Extensive
experiments show that ODN can effectively detect and recognize new categories
with little human intervention, thus applicable to the open-set action
recognition tasks in the real world. Moreover, ODN can even achieve comparable
performance to some closed-set methods.Comment: 6 pages, 3 figures, ICME 201
Angular Stripe Phase in Spin-Orbital-Angular-Momentum Coupled Bose Condensates
We propose that novel superfluid with supersolid-like properties - angular
stripe phase - can be realized in a pancake-like spin-1/2 Bose gas with
spin-orbital-angular-momentum coupling. We predict a rich ground-state phase
diagram, including the vortex-antivortex pair phase, half-skyrmion phase, and
two different angular stripe phases. The stripe phases feature modulated
angular density-density correlation with sizable contrast and can occupy a
relatively large parameter space. The low-lying collective excitations, such as
the dipole and breathing modes, show distinct behaviors in different phases.
The existence of the novel stripe phase is also clearly indicated in the
energetic and dynamic instabilities of collective modes near phase transitions.
Our predictions of the angular stripe phase could be readily examined in
current cold-atom experiments with Rb and K.Comment: 5+3 pages, 4+2 figure
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