Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Self-supervised Augmentation Consistency for Adapting Semantic
  Segmentation   We propose an approach to domain adaptation for semantic segmentation that is
both practical and highly accurate. In contrast to previous work, we abandon
the use of computationally involved adversarial objectives, network ensembles
and style transfer. Instead, we employ standard data augmentation techniques
$-$ photometric noise, flipping and scaling $-$ and ensure consistency of the
semantic predictions across these image transformations. We develop this
principle in a lightweight self-supervised framework trained on co-evolving
pseudo labels without the need for cumbersome extra training rounds. Simple in
training from a practitioner's standpoint, our approach is remarkably
effective. We achieve significant improvements of the state-of-the-art
segmentation accuracy after adaptation, consistent both across different
choices of the backbone architecture and adaptation scenarios.

3
The categories of this paper are: physics
32
The categories of this paper are: atom-ph
17
The categories of this paper are: q-fin
14
The categories of this paper are: math-ph
4
The categories of this paper are: cond-mat
24
The categories of this paper are: dg-ga
21
The categories of this paper are: adap-org
34
The categories of this paper are: chem-ph
12
The categories of this paper are: nucl-th
5
The categories of this paper are: gr-qc
0
The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
33
The categories of this paper are: plasm-ph
35
The categories of this paper are: mtrl-th