Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Scene Gist with Convolutional Neural Networks to Improve Object
  Recognition   Advancements in convolutional neural networks (CNNs) have made significant
strides toward achieving high performance levels on multiple object recognition
tasks. While some approaches utilize information from the entire scene to
propose regions of interest, the task of interpreting a particular region or
object is still performed independently of other objects and features in the
image. Here we demonstrate that a scene's 'gist' can significantly contribute
to how well humans can recognize objects. These findings are consistent with
the notion that humans foveate on an object and incorporate information from
the periphery to aid in recognition. We use a biologically inspired two-part
convolutional neural network ('GistNet') that models the fovea and periphery to
provide a proof-of-principle demonstration that computational object
recognition can significantly benefit from the gist of the scene as contextual
information. Our model yields accuracy improvements of up to 50% in certain
object categories when incorporating contextual gist, while only increasing the
original model size by 5%. This proposed model mirrors our intuition about how
the human visual system recognizes objects, suggesting specific biologically
plausible constraints to improve machine vision and building initial steps
towards the challenge of scene understanding.

6
The categories of this paper are: astro-ph
12
The categories of this paper are: nucl-th
15
The categories of this paper are: nucl-ex
11
The categories of this paper are: hep-lat
19
The categories of this paper are: eess
29
The categories of this paper are: funct-an
33
The categories of this paper are: plasm-ph
30
The categories of this paper are: q-alg
14
The categories of this paper are: math-ph
22
The categories of this paper are: chao-dyn
36
The categories of this paper are: cmp-lg
3
The categories of this paper are: physics
18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
24
The categories of this paper are: dg-ga
32
The categories of this paper are: atom-ph