337 research outputs found
Quantifying Facial Age by Posterior of Age Comparisons
We introduce a novel approach for annotating large quantity of in-the-wild
facial images with high-quality posterior age distribution as labels. Each
posterior provides a probability distribution of estimated ages for a face. Our
approach is motivated by observations that it is easier to distinguish who is
the older of two people than to determine the person's actual age. Given a
reference database with samples of known ages and a dataset to label, we can
transfer reliable annotations from the former to the latter via
human-in-the-loop comparisons. We show an effective way to transform such
comparisons to posterior via fully-connected and SoftMax layers, so as to
permit end-to-end training in a deep network. Thanks to the efficient and
effective annotation approach, we collect a new large-scale facial age dataset,
dubbed `MegaAge', which consists of 41,941 images. Data can be downloaded from
our project page mmlab.ie.cuhk.edu.hk/projects/MegaAge and
github.com/zyx2012/Age_estimation_BMVC2017. With the dataset, we train a
network that jointly performs ordinal hyperplane classification and posterior
distribution learning. Our approach achieves state-of-the-art results on
popular benchmarks such as MORPH2, Adience, and the newly proposed MegaAge.Comment: To appear on BMVC 2017 (oral) revised versio
Nonplanar On-shell Diagrams and Leading Singularities of Scattering Amplitudes
Bipartite on-shell diagrams are the latest tool in constructing scattering
amplitudes. In this paper we prove that a Britto-Cachazo-Feng-Witten
(BCFW)-decomposable on-shell diagram process a rational top-form if and only if
the algebraic ideal comprised of the geometrical constraints is shifted
linearly during successive BCFW integrations. With a proper geometric
interpretation of the constraints in the Grassmannian manifold, the rational
top-form integration contours can thus be obtained, and understood, in a
straightforward way. All rational top-form integrands of arbitrary higher loops
leading singularities can therefore be derived recursively, as long as the
corresponding on-shell diagram is BCFW-decomposable.Comment: 13 pages with 12 figures; final version appeared in Eur.Phys.J. C77
(2017) no.2, 8
Unified Pretraining Target Based Video-music Retrieval With Music Rhythm And Video Optical Flow Information
Background music (BGM) can enhance the video's emotion. However, selecting an
appropriate BGM often requires domain knowledge. This has led to the
development of video-music retrieval techniques. Most existing approaches
utilize pretrained video/music feature extractors trained with different target
sets to obtain average video/music-level embeddings. The drawbacks are
two-fold. One is that different target sets for video/music pretraining may
cause the generated embeddings difficult to match. The second is that the
underlying temporal correlation between video and music is ignored. In this
paper, our proposed approach leverages a unified target set to perform
video/music pretraining and produces clip-level embeddings to preserve temporal
information. The downstream cross-modal matching is based on the clip-level
features with embedded music rhythm and optical flow information. Experiments
demonstrate that our proposed method can achieve superior performance over the
state-of-the-art methods by a significant margin
A Survey on Influence Maximization: From an ML-Based Combinatorial Optimization
Influence Maximization (IM) is a classical combinatorial optimization
problem, which can be widely used in mobile networks, social computing, and
recommendation systems. It aims at selecting a small number of users such that
maximizing the influence spread across the online social network. Because of
its potential commercial and academic value, there are a lot of researchers
focusing on studying the IM problem from different perspectives. The main
challenge comes from the NP-hardness of the IM problem and \#P-hardness of
estimating the influence spread, thus traditional algorithms for overcoming
them can be categorized into two classes: heuristic algorithms and
approximation algorithms. However, there is no theoretical guarantee for
heuristic algorithms, and the theoretical design is close to the limit.
Therefore, it is almost impossible to further optimize and improve their
performance. With the rapid development of artificial intelligence, the
technology based on Machine Learning (ML) has achieved remarkable achievements
in many fields. In view of this, in recent years, a number of new methods have
emerged to solve combinatorial optimization problems by using ML-based
techniques. These methods have the advantages of fast solving speed and strong
generalization ability to unknown graphs, which provide a brand-new direction
for solving combinatorial optimization problems. Therefore, we abandon the
traditional algorithms based on iterative search and review the recent
development of ML-based methods, especially Deep Reinforcement Learning, to
solve the IM problem and other variants in social networks. We focus on
summarizing the relevant background knowledge, basic principles, common
methods, and applied research. Finally, the challenges that need to be solved
urgently in future IM research are pointed out.Comment: 45 page
An Empirical Study on the Holiday Effect of China's Time-Honored Companies
The stock segment of China's time-honored brand enterprises has an important
position in our securities stock market. The holiday effect is one of the
market anomalies that occur in the securities market, which refers to the
phenomenon that the stock market has significantly different returns than other
trading days around festivals. The study of the holiday effect of China's
time-honored brand enterprises can provide fresh ideas for the revitalization
of our time-honored brands and the revitalization of time-honored enterprises.
This paper takes listed companies of China's time-honored brand enterprises as
the research object and focuses on the impact of the holiday effect on listed
companies of China's time-honored brands with the help of the event study, and
empirically analyses the changes in the return of listed companies of China
time-honored brands during the Spring Festival period from 2012 to 2021. The
empirical results reveal that: the time-honored brand concept stocks have a
significant post-holiday effect during the Chinese New Year period, the
time-honored alcoholic beverage enterprises are more sensitive to the Chinese
New Year reflection, while the holiday effect of the time-honored
pharmaceutical manufacturing enterprises is not significant.Comment: 24page
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