Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Semi-Supervised Semantic Segmentation with High- and Low-level
  Consistency   The ability to understand visual information from limited labeled data is an
important aspect of machine learning. While image-level classification has been
extensively studied in a semi-supervised setting, dense pixel-level
classification with limited data has only drawn attention recently. In this
work, we propose an approach for semi-supervised semantic segmentation that
learns from limited pixel-wise annotated samples while exploiting additional
annotation-free images. It uses two network branches that link semi-supervised
classification with semi-supervised segmentation including self-training. The
dual-branch approach reduces both the low-level and the high-level artifacts
typical when training with few labels. The approach attains significant
improvement over existing methods, especially when trained with very few
labeled samples. On several standard benchmarks - PASCAL VOC 2012,
PASCAL-Context, and Cityscapes - the approach achieves new state-of-the-art in
semi-supervised learning.

20
The categories of this paper are: acc-phys
27
The categories of this paper are: comp-gas
13
The categories of this paper are: quant-ph
4
The categories of this paper are: cond-mat
18
The categories of this paper are: econ
12
The categories of this paper are: nucl-th
29
The categories of this paper are: funct-an
24
The categories of this paper are: dg-ga
34
The categories of this paper are: chem-ph
31
The categories of this paper are: ao-sci
3
The categories of this paper are: physics
19
The categories of this paper are: eess
30
The categories of this paper are: q-alg
14
The categories of this paper are: math-ph
32
The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc