12,267 research outputs found
Fine-grained Discriminative Localization via Saliency-guided Faster R-CNN
Discriminative localization is essential for fine-grained image
classification task, which devotes to recognizing hundreds of subcategories in
the same basic-level category. Reflecting on discriminative regions of objects,
key differences among different subcategories are subtle and local. Existing
methods generally adopt a two-stage learning framework: The first stage is to
localize the discriminative regions of objects, and the second is to encode the
discriminative features for training classifiers. However, these methods
generally have two limitations: (1) Separation of the two-stage learning is
time-consuming. (2) Dependence on object and parts annotations for
discriminative localization learning leads to heavily labor-consuming labeling.
It is highly challenging to address these two important limitations
simultaneously. Existing methods only focus on one of them. Therefore, this
paper proposes the discriminative localization approach via saliency-guided
Faster R-CNN to address the above two limitations at the same time, and our
main novelties and advantages are: (1) End-to-end network based on Faster R-CNN
is designed to simultaneously localize discriminative regions and encode
discriminative features, which accelerates classification speed. (2)
Saliency-guided localization learning is proposed to localize the
discriminative region automatically, avoiding labor-consuming labeling. Both
are jointly employed to simultaneously accelerate classification speed and
eliminate dependence on object and parts annotations. Comparing with the
state-of-the-art methods on the widely-used CUB-200-2011 dataset, our approach
achieves both the best classification accuracy and efficiency.Comment: 9 pages, to appear in ACM MM 201
Interconnecting bilayer networks
A typical complex system should be described by a supernetwork or a network
of networks, in which the networks are coupled to some other networks. As the
first step to understanding the complex systems on such more systematic level,
scientists studied interdependent multilayer networks. In this letter, we
introduce a new kind of interdependent multilayer networks, i.e.,
interconnecting networks, for which the component networks are coupled each
other by sharing some common nodes. Based on the empirical investigations, we
revealed a common feature of such interconnecting networks, namely, the
networks with smaller averaged topological differences of the interconnecting
nodes tend to share more nodes. A very simple node sharing mechanism is
proposed to analytically explain the observed feature of the interconnecting
networks.Comment: 9 page
Will Sentiment Analysis Need Subculture? A New Data Augmentation Approach
The renowned proverb that "The pen is mightier than the sword" underscores
the formidable influence wielded by text expressions in shaping sentiments.
Indeed, well-crafted written can deeply resonate within cultures, conveying
profound sentiments. Nowadays, the omnipresence of the Internet has fostered a
subculture that congregates around the contemporary milieu. The subculture
artfully articulates the intricacies of human feelings by ardently pursuing the
allure of novelty, a fact that cannot be disregarded in the sentiment analysis.
This paper strives to enrich data through the lens of subculture, to address
the insufficient training data faced by sentiment analysis. To this end, a new
approach of subculture-based data augmentation (SCDA) is proposed, which
engenders six enhanced texts for each training text by leveraging the creation
of six diverse subculture expression generators. The extensive experiments
attest to the effectiveness and potential of SCDA. The results also shed light
on the phenomenon that disparate subculture expressions elicit varying degrees
of sentiment stimulation. Moreover, an intriguing conjecture arises, suggesting
the linear reversibility of certain subculture expressions. It is our fervent
aspiration that this study serves as a catalyst in fostering heightened
perceptiveness towards the tapestry of information, sentiment and culture,
thereby enriching our collective understanding.Comment: JASIS
Enhancement of EMAT’s efficiency by using silicon steel laminations back-plate
Silicon steel laminations are introduced as the back-plate to an electromagnetic acoustic transducer (EMAT) to increase the efficiency of the EMAT by increasing the magnitude of the EMAT coil's dynamic magnetic field and the eddy current in the sample surface. A two-dimensional, non-linear finite element model is developed to quantify the effectiveness of the back-plate’s different maximum permeability and saturation flux density, on increasing the eddy current density and the dynamic magnetic flux density in the specimen. A three-dimensional FE model is also developed, and confirms the expected result that the laminated structure of silicon steel (SiFe) markedly reduces the eddy current induced in the back-plate, when compared to a continuous slab of the steel. Experimental results show that silicon steel lamination can increase the efficiency of the EMAT in the cases both with and without a biasing magnetic field
Resonant cyclotron scattering in pulsar magnetospheres and its application to isolated neutron stars
Resonant cyclotron scattering (RCS) in pulsar magnetospheres is considered.
The photon diffusion equation (Kompaneets equation) for RCS is derived. The
photon system is modeled three dimensionally. Numerical calculations show that
there exist not only up scattering but also down scattering of RCS, depending
on the parameter space. RCS's possible applications to the spectra energy
distributions of magnetar candidates and radio quiet isolated neutron stars
(INSs) are point out. The optical/UV excess of INSs may caused by the down
scattering of RCS. The calculations for RX J1856.5-3754 and RX J0720.4-3125 are
presented and compared with their observational data. In our model, the INSs
are proposed to be normal neutron stars, although the quark star hypothesis is
still possible. The low pulsation amplitude of INSs is a natural consequence in
the RCS model.Comment: 16 pages, 5 figures, 2 tables, accepted for publication in RA
A generalized public goods game with coupling of individual ability and project benefit
Facing a heavy task, any single person can only make a limited contribution
and team cooperation is needed. As one enjoys the benefit of the public goods,
the potential benefits of the project are not always maximized and may be
partly wasted. By incorporating individual ability and project benefit into the
original public goods game, we study the coupling effect of the four
parameters, the upper limit of individual contribution, the upper limit of
individual benefit, the needed project cost and the upper limit of project
benefit on the evolution of cooperation. Coevolving with the individual-level
group size preferences, an increase in the upper limit of individual benefit
promotes cooperation while an increase in the upper limit of individual
contribution inhibits cooperation. The coupling of the upper limit of
individual contribution and the needed project cost determines the critical
point of the upper limit of project benefit, where the equilibrium frequency of
cooperators reaches its highest level. Above the critical point, an increase in
the upper limit of project benefit inhibits cooperation. The evolution of
cooperation is closely related to the preferred group-size distribution. A
functional relation between the frequency of cooperators and the dominant group
size is found
Fine-grained Domain Adaptive Crowd Counting via Point-derived Segmentation
Due to domain shift, a large performance drop is usually observed when a
trained crowd counting model is deployed in the wild. While existing
domain-adaptive crowd counting methods achieve promising results, they
typically regard each crowd image as a whole and reduce domain discrepancies in
a holistic manner, thus limiting further improvement of domain adaptation
performance. To this end, we propose to untangle \emph{domain-invariant} crowd
and \emph{domain-specific} background from crowd images and design a
fine-grained domain adaption method for crowd counting. Specifically, to
disentangle crowd from background, we propose to learn crowd segmentation from
point-level crowd counting annotations in a weakly-supervised manner. Based on
the derived segmentation, we design a crowd-aware domain adaptation mechanism
consisting of two crowd-aware adaptation modules, i.e., Crowd Region Transfer
(CRT) and Crowd Density Alignment (CDA). The CRT module is designed to guide
crowd features transfer across domains beyond background distractions. The CDA
module dedicates to regularising target-domain crowd density generation by its
own crowd density distribution. Our method outperforms previous approaches
consistently in the widely-used adaptation scenarios.Comment: 10 pages, 5 figures, and 9 table
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