Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Weakly Supervised Instance Segmentation Using Hybrid Network   Weakly-supervised instance segmentation, which could greatly save labor and
time cost of pixel mask annotation, has attracted increasing attention in
recent years. The commonly used pipeline firstly utilizes conventional image
segmentation methods to automatically generate initial masks and then use them
to train an off-the-shelf segmentation network in an iterative way. However,
the initial generated masks usually contains a notable proportion of invalid
masks which are mainly caused by small object instances. Directly using these
initial masks to train segmentation model is harmful for the performance. To
address this problem, we propose a hybrid network in this paper. In our
architecture, there is a principle segmentation network which is used to handle
the normal samples with valid generated masks. In addition, a complementary
branch is added to handle the small and dim objects without valid masks.
Experimental results indicate that our method can achieve significantly
performance improvement both on the small object instances and large ones, and
outperforms all state-of-the-art methods.

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The categories of this paper are: q-alg
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The categories of this paper are: econ
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The categories of this paper are: atom-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: hep-ex
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The categories of this paper are: ao-sci
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The categories of this paper are: solv-int
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The categories of this paper are: plasm-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: nlin
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The categories of this paper are: hep-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: comp-gas
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The categories of this paper are: funct-an
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The categories of this paper are: dg-ga
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The categories of this paper are: q-fin