Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Curved Text Detection in Natural Scene Images with Semi- and
  Weakly-Supervised Learning   Detecting curved text in the wild is very challenging. Recently, most
state-of-the-art methods are segmentation based and require pixel-level
annotations. We propose a novel scheme to train an accurate text detector using
only a small amount of pixel-level annotated data and a large amount of data
annotated with rectangles or even unlabeled data. A baseline model is first
obtained by training with the pixel-level annotated data and then used to
annotate unlabeled or weakly labeled data. A novel strategy which utilizes
ground-truth bounding boxes to generate pseudo mask annotations is proposed in
weakly-supervised learning. Experimental results on CTW1500 and Total-Text
demonstrate that our method can substantially reduce the requirement of
pixel-level annotated data. Our method can also generalize well across two
datasets. The performance of the proposed method is comparable with the
state-of-the-art methods with only 10% pixel-level annotated data and 90%
rectangle-level weakly annotated data.

12
The categories of this paper are: nucl-th
2
The categories of this paper are: math
15
The categories of this paper are: nucl-ex
37
The categories of this paper are: supr-con
0
The categories of this paper are: hep-ph
35
The categories of this paper are: mtrl-th
24
The categories of this paper are: dg-ga
14
The categories of this paper are: math-ph
8
The categories of this paper are: hep-ex
33
The categories of this paper are: plasm-ph
22
The categories of this paper are: chao-dyn
20
The categories of this paper are: acc-phys
5
The categories of this paper are: gr-qc
19
The categories of this paper are: eess
7
The categories of this paper are: hep-th
30
The categories of this paper are: q-alg