Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Embracing New Techniques in Deep Learning for Estimating Image
  Memorability   Various work has suggested that the memorability of an image is consistent
across people, and thus can be treated as an intrinsic property of an image.
Using computer vision models, we can make specific predictions about what
people will remember or forget. While older work has used now-outdated deep
learning architectures to predict image memorability, innovations in the field
have given us new techniques to apply to this problem. Here, we propose and
evaluate five alternative deep learning models which exploit developments in
the field from the last five years, largely the introduction of residual neural
networks, which are intended to allow the model to use semantic information in
the memorability estimation process. These new models were tested against the
prior state of the art with a combined dataset built to optimize both
within-category and across-category predictions. Our findings suggest that the
key prior memorability network had overstated its generalizability and was
overfit on its training set. Our new models outperform this prior model,
leading us to conclude that Residual Networks outperform simpler convolutional
neural networks in memorability regression. We make our new state-of-the-art
model readily available to the research community, allowing memory researchers
to make predictions about memorability on a wider range of images.

21
The categories of this paper are: adap-org
12
The categories of this paper are: nucl-th
37
The categories of this paper are: supr-con
0
The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph
4
The categories of this paper are: cond-mat
25
The categories of this paper are: patt-sol
6
The categories of this paper are: astro-ph
26
The categories of this paper are: bayes-an
10
The categories of this paper are: q-bio
3
The categories of this paper are: physics
7
The categories of this paper are: hep-th
33
The categories of this paper are: plasm-ph
36
The categories of this paper are: cmp-lg
20
The categories of this paper are: acc-phys
30
The categories of this paper are: q-alg