Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Instance segmentation with the number of clusters incorporated in
  embedding learning   Semantic and instance segmentation algorithms are two general yet distinct
image segmentation solutions powered by Convolution Neural Network. While
semantic segmentation benefits extensively from the end-to-end training
strategy, instance segmentation is frequently framed as a multi-stage task,
supported by learning-based discrimination and post-process clustering.
Independent optimizations on substages instigate the accumulation of
segmentation errors. In this work, we propose to embed prior clustering
information into an embedding learning framework FCRNet, stimulating the
one-stage instance segmentation. FCRNet relieves the complexity of post process
by incorporating the number of clustering groups into the embedding space. The
superior performance of FCRNet is verified and compared with other methods on
the nucleus dataset BBBC006.

20
The categories of this paper are: acc-phys
12
The categories of this paper are: nucl-th
37
The categories of this paper are: supr-con
23
The categories of this paper are: solv-int
24
The categories of this paper are: dg-ga
27
The categories of this paper are: comp-gas
16
The categories of this paper are: stat
5
The categories of this paper are: gr-qc
2
The categories of this paper are: math
21
The categories of this paper are: adap-org
35
The categories of this paper are: mtrl-th
26
The categories of this paper are: bayes-an
18
The categories of this paper are: econ
36
The categories of this paper are: cmp-lg
9
The categories of this paper are: nlin
15
The categories of this paper are: nucl-ex