Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DFPN: Deformable Frame Prediction Network   Learned frame prediction is a current problem of interest in computer vision
and video compression. Although several deep network architectures have been
proposed for learned frame prediction, to the best of our knowledge, there is
no work based on using deformable convolutions for frame prediction. To this
effect, we propose a deformable frame prediction network (DFPN) for task
oriented implicit motion modeling and next frame prediction. Experimental
results demonstrate that the proposed DFPN model achieves state of the art
results in next frame prediction. Our models and results are available at
https://github.com/makinyilmaz/DFPN.

8
The categories of this paper are: hep-ex
29
The categories of this paper are: funct-an
7
The categories of this paper are: hep-th
20
The categories of this paper are: acc-phys
9
The categories of this paper are: nlin
34
The categories of this paper are: chem-ph
12
The categories of this paper are: nucl-th
14
The categories of this paper are: math-ph
21
The categories of this paper are: adap-org
26
The categories of this paper are: bayes-an
6
The categories of this paper are: astro-ph
37
The categories of this paper are: supr-con
18
The categories of this paper are: econ
33
The categories of this paper are: plasm-ph
16
The categories of this paper are: stat
28
The categories of this paper are: alg-geom