Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Geometric Neural Phrase Pooling: Modeling the Spatial Co-occurrence of
  Neurons   Deep Convolutional Neural Networks (CNNs) are playing important roles in
state-of-the-art visual recognition. This paper focuses on modeling the spatial
co-occurrence of neuron responses, which is less studied in the previous work.
For this, we consider the neurons in the hidden layer as neural words, and
construct a set of geometric neural phrases on top of them. The idea that
grouping neural words into neural phrases is borrowed from the
Bag-of-Visual-Words (BoVW) model. Next, the Geometric Neural Phrase Pooling
(GNPP) algorithm is proposed to efficiently encode these neural phrases. GNPP
acts as a new type of hidden layer, which punishes the isolated neuron
responses after convolution, and can be inserted into a CNN model with little
extra computational overhead. Experimental results show that GNPP produces
significant and consistent accuracy gain in image classification.

22
The categories of this paper are: chao-dyn
34
The categories of this paper are: chem-ph
24
The categories of this paper are: dg-ga
6
The categories of this paper are: astro-ph
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
20
The categories of this paper are: acc-phys
17
The categories of this paper are: q-fin
9
The categories of this paper are: nlin
30
The categories of this paper are: q-alg
31
The categories of this paper are: ao-sci
3
The categories of this paper are: physics
35
The categories of this paper are: mtrl-th
2
The categories of this paper are: math
11
The categories of this paper are: hep-lat
8
The categories of this paper are: hep-ex