Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Recurrent U-Net for Resource-Constrained Segmentation   State-of-the-art segmentation methods rely on very deep networks that are not
always easy to train without very large training datasets and tend to be
relatively slow to run on standard GPUs. In this paper, we introduce a novel
recurrent U-Net architecture that preserves the compactness of the original
U-Net, while substantially increasing its performance to the point where it
outperforms the state of the art on several benchmarks. We will demonstrate its
effectiveness for several tasks, including hand segmentation, retina vessel
segmentation, and road segmentation. We also introduce a large-scale dataset
for hand segmentation.

36
The categories of this paper are: cmp-lg
33
The categories of this paper are: plasm-ph
18
The categories of this paper are: econ
14
The categories of this paper are: math-ph
24
The categories of this paper are: dg-ga
35
The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
20
The categories of this paper are: acc-phys
13
The categories of this paper are: quant-ph
2
The categories of this paper are: math
0
The categories of this paper are: hep-ph
16
The categories of this paper are: stat
12
The categories of this paper are: nucl-th
22
The categories of this paper are: chao-dyn
4
The categories of this paper are: cond-mat
3
The categories of this paper are: physics