9,088 research outputs found
PersonRank: Detecting Important People in Images
Always, some individuals in images are more important/attractive than others
in some events such as presentation, basketball game or speech. However, it is
challenging to find important people among all individuals in images directly
based on their spatial or appearance information due to the existence of
diverse variations of pose, action, appearance of persons and various changes
of occasions. We overcome this difficulty by constructing a multiple
Hyper-Interaction Graph to treat each individual in an image as a node and
inferring the most active node referring to interactions estimated by various
types of clews. We model pairwise interactions between persons as the edge
message communicated between nodes, resulting in a bidirectional
pairwise-interaction graph. To enrich the personperson interaction estimation,
we further introduce a unidirectional hyper-interaction graph that models the
consensus of interaction between a focal person and any person in a local
region around. Finally, we modify the PageRank algorithm to infer the
activeness of persons on the multiple Hybrid-Interaction Graph (HIG), the union
of the pairwise-interaction and hyperinteraction graphs, and we call our
algorithm the PersonRank. In order to provide publicable datasets for
evaluation, we have contributed a new dataset called Multi-scene Important
People Image Dataset and gathered a NCAA Basketball Image Dataset from sports
game sequences. We have demonstrated that the proposed PersonRank outperforms
related methods clearly and substantially.Comment: 8 pages, conferenc
Is a molecular state
Assuming the newly observed to be a molecular state of , we calculate the partial widths of and within the light front
model (LFM). is the channel by which was
observed, our calculation indicates that it is indeed one of the dominant modes
whose width can be in the range of a few MeV depending on the model parameters.
Similar to and , Voloshin suggested that there should be a
resonance at 4030 MeV which can be a molecular state of .
Then we go on calculating its decay rates to all the aforementioned final
states and as well the . It is found that if is a
molecular state of , the partial width of
is rather small, but the rate of
is even larger than . The
implications are discussed and it is indicated that with the luminosity of BES
and BELLE, the experiments may finally determine if is a molecular
state or a tetraquark.Comment: 17 pages, 6 figures, 3 table
Re-Study on the wave functions of states in LFQM and the radiative decays of
The Light-front quark model (LFQM) has been applied to calculate the
transition matrix elements of heavy hadron decays. However, it is noted that
using the traditional wave functions of the LFQM given in literature, the
theoretically determined decay constants of the obviously
contradict to the data. It implies that the wave functions must be modified.
Keeping the orthogonality among the states and fitting their decay
constants we obtain a series of the wave functions for . Based on
these wave functions and by analogy to the hydrogen atom, we suggest a modified
analytical form for the wave functions. By use of the modified
wave functions, the obtained decay constants are close to the experimental
data. Then we calculate the rates of radiative decays of . Our predictions are consistent with the experimental data on
decays within the theoretical and experimental
errors.Comment: 10 pages, 2 figures, 1 table. Typos corrected and more discussions
added. accepted for publication in Physical Review
Learning to Detect Important People in Unlabelled Images for Semi-supervised Important People Detection
Important people detection is to automatically detect the individuals who
play the most important roles in a social event image, which requires the
designed model to understand a high-level pattern. However, existing methods
rely heavily on supervised learning using large quantities of annotated image
samples, which are more costly to collect for important people detection than
for individual entity recognition (eg, object recognition). To overcome this
problem, we propose learning important people detection on partially annotated
images. Our approach iteratively learns to assign pseudo-labels to individuals
in un-annotated images and learns to update the important people detection
model based on data with both labels and pseudo-labels. To alleviate the
pseudo-labelling imbalance problem, we introduce a ranking strategy for
pseudo-label estimation, and also introduce two weighting strategies: one for
weighting the confidence that individuals are important people to strengthen
the learning on important people and the other for neglecting noisy unlabelled
images (ie, images without any important people). We have collected two
large-scale datasets for evaluation. The extensive experimental results clearly
confirm the efficacy of our method attained by leveraging unlabelled images for
improving the performance of important people detection
Probing signatures of bounce inflation with current observations
The aim of this paper is to probe the features of the bouncing cosmology with
the current observational data. Basing on bounce inflation model, with high
derivative term, we propose a general parametrization of primordial power
spectrum which includes the typical bouncing parameters, such as bouncing
time-scale, and energy scale. By applying Markov Chain Monto Carlo analysis
with current data combination of Planck 2015, BAO and JLA, we report the
posterior probability distributions of the parameters. We find that, bouncing
models can well explain CMB observations, especially the deficit and
oscillation on large scale in TT power spectrum.Comment: 17 pages, 8 figure
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