18,146 research outputs found
Optimal Relay Selection with Non-negligible Probing Time
In this paper an optimal relay selection algorithm with non-negligible
probing time is proposed and analyzed for cooperative wireless networks. Relay
selection has been introduced to solve the degraded bandwidth efficiency
problem in cooperative communication. Yet complete information of relay
channels often remain unavailable for complex networks which renders the
optimal selection strategies impossible for transmission source without probing
the relay channels. Particularly when the number of relay candidate is large,
even though probing all relay channels guarantees the finding of the best
relays at any time instant, the degradation of bandwidth efficiency due to
non-negligible probing times, which was often neglected in past literature, is
also significant. In this work, a stopping rule based relay selection strategy
is determined for the source node to decide when to stop the probing process
and choose one of the probed relays to cooperate with under wireless channels'
stochastic uncertainties. This relay selection strategy is further shown to
have a simple threshold structure. At the meantime, full diversity order and
high bandwidth efficiency can be achieved simultaneously. Both analytical and
simulation results are provided to verify the claims.Comment: 8 pages. ICC 201
Quality Aware Network for Set to Set Recognition
This paper targets on the problem of set to set recognition, which learns the
metric between two image sets. Images in each set belong to the same identity.
Since images in a set can be complementary, they hopefully lead to higher
accuracy in practical applications. However, the quality of each sample cannot
be guaranteed, and samples with poor quality will hurt the metric. In this
paper, the quality aware network (QAN) is proposed to confront this problem,
where the quality of each sample can be automatically learned although such
information is not explicitly provided in the training stage. The network has
two branches, where the first branch extracts appearance feature embedding for
each sample and the other branch predicts quality score for each sample.
Features and quality scores of all samples in a set are then aggregated to
generate the final feature embedding. We show that the two branches can be
trained in an end-to-end manner given only the set-level identity annotation.
Analysis on gradient spread of this mechanism indicates that the quality
learned by the network is beneficial to set-to-set recognition and simplifies
the distribution that the network needs to fit. Experiments on both face
verification and person re-identification show advantages of the proposed QAN.
The source code and network structure can be downloaded at
https://github.com/sciencefans/Quality-Aware-Network.Comment: Accepted at CVPR 201
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