Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: AdaptIS: Adaptive Instance Selection Network   We present Adaptive Instance Selection network architecture for
class-agnostic instance segmentation. Given an input image and a point $(x,
y)$, it generates a mask for the object located at $(x, y)$. The network adapts
to the input point with a help of AdaIN layers, thus producing different masks
for different objects on the same image. AdaptIS generates pixel-accurate
object masks, therefore it accurately segments objects of complex shape or
severely occluded ones. AdaptIS can be easily combined with standard semantic
segmentation pipeline to perform panoptic segmentation. To illustrate the idea,
we perform experiments on a challenging toy problem with difficult occlusions.
Then we extensively evaluate the method on panoptic segmentation benchmarks. We
obtain state-of-the-art results on Cityscapes and Mapillary even without
pretraining on COCO, and show competitive results on a challenging COCO
dataset. The source code of the method and the trained models are available at
https://github.com/saic-vul/adaptis.

28
The categories of this paper are: alg-geom
34
The categories of this paper are: chem-ph
33
The categories of this paper are: plasm-ph
37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph
23
The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th
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The categories of this paper are: nucl-ex
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The categories of this paper are: quant-ph
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The categories of this paper are: eess
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The categories of this paper are: bayes-an
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The categories of this paper are: mtrl-th
10
The categories of this paper are: q-bio