Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Classification of Occluded Objects using Fast Recurrent Processing   Recurrent neural networks are powerful tools for handling incomplete data
problems in computer vision, thanks to their significant generative
capabilities. However, the computational demand for these algorithms is too
high to work in real time, without specialized hardware or software solutions.
In this paper, we propose a framework for augmenting recurrent processing
capabilities into a feedforward network without sacrificing much from
computational efficiency. We assume a mixture model and generate samples of the
last hidden layer according to the class decisions of the output layer, modify
the hidden layer activity using the samples, and propagate to lower layers. For
visual occlusion problem, the iterative procedure emulates feedforward-feedback
loop, filling-in the missing hidden layer activity with meaningful
representations. The proposed algorithm is tested on a widely used dataset, and
shown to achieve 2$\times$ improvement in classification accuracy for occluded
objects. When compared to Restricted Boltzmann Machines, our algorithm shows
superior performance for occluded object classification.

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The categories of this paper are: bayes-an
31
The categories of this paper are: ao-sci
11
The categories of this paper are: hep-lat
32
The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc
15
The categories of this paper are: nucl-ex
18
The categories of this paper are: econ
21
The categories of this paper are: adap-org
30
The categories of this paper are: q-alg
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The categories of this paper are: comp-gas
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The categories of this paper are: solv-int
34
The categories of this paper are: chem-ph
12
The categories of this paper are: nucl-th
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The categories of this paper are: eess
37
The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex