Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Learning for Vision-based Prediction: A Survey   Vision-based prediction algorithms have a wide range of applications
including autonomous driving, surveillance, human-robot interaction, weather
prediction. The objective of this paper is to provide an overview of the field
in the past five years with a particular focus on deep learning approaches. For
this purpose, we categorize these algorithms into video prediction, action
prediction, trajectory prediction, body motion prediction, and other prediction
applications. For each category, we highlight the common architectures,
training methods and types of data used. In addition, we discuss the common
evaluation metrics and datasets used for vision-based prediction tasks. A
database of all the information presented in this survey including,
cross-referenced according to papers, datasets and metrics, can be found online
at https://github.com/aras62/vision-based-prediction.

12
The categories of this paper are: nucl-th
28
The categories of this paper are: alg-geom
15
The categories of this paper are: nucl-ex
6
The categories of this paper are: astro-ph
22
The categories of this paper are: chao-dyn
16
The categories of this paper are: stat
10
The categories of this paper are: q-bio
37
The categories of this paper are: supr-con
34
The categories of this paper are: chem-ph
2
The categories of this paper are: math
18
The categories of this paper are: econ
5
The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th
31
The categories of this paper are: ao-sci
32
The categories of this paper are: atom-ph
4
The categories of this paper are: cond-mat