Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Energy Based Inpainting for Optical Flow   Modern optical flow methods are often composed of a cascade of many
independent steps or formulated as a black box neural network that is hard to
interpret and analyze. In this work we seek for a plain, interpretable, but
learnable solution. We propose a novel inpainting based algorithm that
approaches the problem in three steps: feature selection and matching,
selection of supporting points and energy based inpainting. To facilitate the
inference we propose an optimization layer that allows to backpropagate through
10K iterations of a first-order method without any numerical or memory
problems. Compared to recent state-of-the-art networks, our modular CNN is very
lightweight and competitive with other, more involved, inpainting based
methods.

37
The categories of this paper are: supr-con
11
The categories of this paper are: hep-lat
13
The categories of this paper are: quant-ph
33
The categories of this paper are: plasm-ph
10
The categories of this paper are: q-bio
17
The categories of this paper are: q-fin
24
The categories of this paper are: dg-ga
14
The categories of this paper are: math-ph
12
The categories of this paper are: nucl-th
28
The categories of this paper are: alg-geom
16
The categories of this paper are: stat
19
The categories of this paper are: eess
2
The categories of this paper are: math
29
The categories of this paper are: funct-an
4
The categories of this paper are: cond-mat
22
The categories of this paper are: chao-dyn