Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Reinforced Temporal Attention and Split-Rate Transfer for Depth-Based
  Person Re-Identification   We address the problem of person re-identification from commodity depth
sensors. One challenge for depth-based recognition is data scarcity. Our first
contribution addresses this problem by introducing split-rate RGB-to-Depth
transfer, which leverages large RGB datasets more effectively than popular
fine-tuning approaches. Our transfer scheme is based on the observation that
the model parameters at the bottom layers of a deep convolutional neural
network can be directly shared between RGB and depth data while the remaining
layers need to be fine-tuned rapidly. Our second contribution enhances
re-identification for video by implementing temporal attention as a
Bernoulli-Sigmoid unit acting upon frame-level features. Since this unit is
stochastic, the temporal attention parameters are trained using reinforcement
learning. Extensive experiments validate the accuracy of our method in person
re-identification from depth sequences. Finally, in a scenario where subjects
wear unseen clothes, we show large performance gains compared to a
state-of-the-art model which relies on RGB data.

9
The categories of this paper are: nlin
7
The categories of this paper are: hep-th
0
The categories of this paper are: hep-ph
21
The categories of this paper are: adap-org
19
The categories of this paper are: eess
5
The categories of this paper are: gr-qc
11
The categories of this paper are: hep-lat
14
The categories of this paper are: math-ph
33
The categories of this paper are: plasm-ph
13
The categories of this paper are: quant-ph
37
The categories of this paper are: supr-con
27
The categories of this paper are: comp-gas
15
The categories of this paper are: nucl-ex
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The categories of this paper are: q-alg
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The categories of this paper are: astro-ph
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The categories of this paper are: physics