Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Content-Aware Unsupervised Deep Homography Estimation   Homography estimation is a basic image alignment method in many applications.
It is usually conducted by extracting and matching sparse feature points, which
are error-prone in low-light and low-texture images. On the other hand,
previous deep homography approaches use either synthetic images for supervised
learning or aerial images for unsupervised learning, both ignoring the
importance of handling depth disparities and moving objects in real world
applications. To overcome these problems, in this work we propose an
unsupervised deep homography method with a new architecture design. In the
spirit of the RANSAC procedure in traditional methods, we specifically learn an
outlier mask to only select reliable regions for homography estimation. We
calculate loss with respect to our learned deep features instead of directly
comparing image content as did previously. To achieve the unsupervised
training, we also formulate a novel triplet loss customized for our network. We
verify our method by conducting comprehensive comparisons on a new dataset that
covers a wide range of scenes with varying degrees of difficulties for the
task. Experimental results reveal that our method outperforms the
state-of-the-art including deep solutions and feature-based solutions.

11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
2
The categories of this paper are: math
19
The categories of this paper are: eess
4
The categories of this paper are: cond-mat
12
The categories of this paper are: nucl-th
33
The categories of this paper are: plasm-ph
13
The categories of this paper are: quant-ph
21
The categories of this paper are: adap-org
9
The categories of this paper are: nlin
34
The categories of this paper are: chem-ph
31
The categories of this paper are: ao-sci
20
The categories of this paper are: acc-phys
18
The categories of this paper are: econ
37
The categories of this paper are: supr-con
10
The categories of this paper are: q-bio