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Commonality-Parsing Network Across Shape and Appearance for Partially Supervised Instance Segmentation

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 12353)

Abstract

Partially supervised instance segmentation aims to perform learning on limited mask-annotated categories of data thus eliminating expensive and exhaustive mask annotation. The learned models are expected to be generalizable to novel categories. Existing methods either learn a transfer function from detection to segmentation, or cluster shape priors for segmenting novel categories. We propose to learn the underlying class-agnostic commonalities that can be generalized from mask-annotated categories to novel categories. Specifically, we parse two types of commonalities: 1) shape commonalities which are learned by performing supervised learning on instance boundary prediction; and 2) appearance commonalities which are captured by modeling pairwise affinities among pixels of feature maps to optimize the separability between instance and the background. Incorporating both the shape and appearance commonalities, our model significantly outperforms the state-of-the-art methods on both partially supervised setting and few-shot setting for instance segmentation on COCO dataset. The code is available at https://github.com/fanq15/FewX.

Keywords

Partially supervised Few-shot Instance segmentation 

Notes

Acknowledgements

This research is supported in part by the Research Grant Council of the Hong Kong SAR under grant no. 1620818.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Hong Kong University of Science and TechnologyClear Water BayHong Kong
  2. 2.Harbin Institute of Technology, ShenzhenShenzhenChina
  3. 3.Kwai Inc.BeijingChina

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