Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Discriminative convolutional Fisher vector network for action
  recognition   In this work we propose a novel neural network architecture for the problem
of human action recognition in videos. The proposed architecture expresses the
processing steps of classical Fisher vector approaches, that is dimensionality
reduction by principal component analysis (PCA) projection, Gaussian mixture
model (GMM) and Fisher vector descriptor extraction, as network layers. By
contrast to other methods where these steps are performed consecutively and the
corresponding parameters are learned in an unsupervised manner, having them
defined as a single neural network allows us to refine the whole model
discriminatively in an end to end fashion. Furthermore, we show that the
proposed architecture can be used as a replacement for the fully connected
layers in popular convolutional networks achieving a comparable classification
performance, or even significantly surpassing the performance of similar
architectures while reducing the total number of trainable parameters by a
factor of 5. We show that our method achieves significant improvements in
comparison to the classical chain.

4
The categories of this paper are: cond-mat
35
The categories of this paper are: mtrl-th
18
The categories of this paper are: econ
8
The categories of this paper are: hep-ex
36
The categories of this paper are: cmp-lg
9
The categories of this paper are: nlin
24
The categories of this paper are: dg-ga
22
The categories of this paper are: chao-dyn
15
The categories of this paper are: nucl-ex
34
The categories of this paper are: chem-ph
25
The categories of this paper are: patt-sol
33
The categories of this paper are: plasm-ph
28
The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
30
The categories of this paper are: q-alg
26
The categories of this paper are: bayes-an