Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Image Alignment in Unseen Domains via Domain Deep Generalization   Image alignment across domains has recently become one of the realistic and
popular topics in the research community. In this problem, a deep
learning-based image alignment method is usually trained on an available
largescale database. During the testing steps, this trained model is deployed
on unseen images collected under different camera conditions and modalities.
The delivered deep network models are unable to be updated, adapted or
fine-tuned in these scenarios. Thus, recent deep learning techniques, e.g.
domain adaptation, feature transferring, and fine-tuning, are unable to be
deployed. This paper presents a novel deep learning based approach to tackle
the problem of across unseen modalities. The proposed network is then applied
to image alignment as an illustration. The proposed approach is designed as an
end-to-end deep convolutional neural network to optimize the deep models to
improve the performance. The proposed network has been evaluated in digit
recognition when the model is trained on MNIST and then tested on unseen domain
MNIST-M. Finally, the proposed method is benchmarked in image alignment problem
when training on RGB images and testing on Depth and X-Ray images.

15
The categories of this paper are: nucl-ex
11
The categories of this paper are: hep-lat
13
The categories of this paper are: quant-ph
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
20
The categories of this paper are: acc-phys
10
The categories of this paper are: q-bio
7
The categories of this paper are: hep-th
36
The categories of this paper are: cmp-lg
17
The categories of this paper are: q-fin
21
The categories of this paper are: adap-org
24
The categories of this paper are: dg-ga
8
The categories of this paper are: hep-ex
16
The categories of this paper are: stat
5
The categories of this paper are: gr-qc
37
The categories of this paper are: supr-con