Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A survey on Kornia: an Open Source Differentiable Computer Vision
  Library for PyTorch   This work presents Kornia, an open source computer vision library built upon
a set of differentiable routines and modules that aims to solve generic
computer vision problems. The package uses PyTorch as its main backend, not
only for efficiency but also to take advantage of the reverse
auto-differentiation engine to define and compute the gradient of complex
functions. Inspired by OpenCV, Kornia is composed of a set of modules
containing operators that can be integrated into neural networks to train
models to perform a wide range of operations including image
transformations,camera calibration, epipolar geometry, and low level image
processing techniques, such as filtering and edge detection that operate
directly on high dimensional tensor representations on graphical processing
units, generating faster systems. Examples of classical vision problems
implemented using our framework are provided including a benchmark comparing to
existing vision libraries.

23
The categories of this paper are: solv-int
34
The categories of this paper are: chem-ph
9
The categories of this paper are: nlin
35
The categories of this paper are: mtrl-th
32
The categories of this paper are: atom-ph
3
The categories of this paper are: physics
25
The categories of this paper are: patt-sol
13
The categories of this paper are: quant-ph
24
The categories of this paper are: dg-ga
11
The categories of this paper are: hep-lat
16
The categories of this paper are: stat
4
The categories of this paper are: cond-mat
22
The categories of this paper are: chao-dyn
7
The categories of this paper are: hep-th
21
The categories of this paper are: adap-org
29
The categories of this paper are: funct-an