Layered embeddings for amodal instance segmentation

Yanfeng Liu, Eric T. Psota, Lance C. Pérez

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


The proposed method extends upon the representational output of semantic instance segmentation by explicitly including both visible and occluded parts. A fully convolutional network is trained to produce consistent pixel-level embedding across two layers such that, when clustered, the results convey the full spatial extent and depth ordering of each instance. Results demonstrate that the network can accurately estimate complete masks in the presence of occlusion and outperform leading top-down bounding-box approaches. Source code available at

Original languageEnglish (US)
Title of host publicationImage Analysis and Recognition - 16th International Conference, ICIAR 2019, Proceedings
EditorsFakhri Karray, Alfred Yu, Aurélio Campilho
PublisherSpringer Verlag
Number of pages10
ISBN (Print)9783030272012
StatePublished - 2019
Event16th International Conference on Image Analysis and Recognition, ICIAR 2019 - Waterloo, Canada
Duration: Aug 27 2019Aug 29 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11662 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference16th International Conference on Image Analysis and Recognition, ICIAR 2019


  • Amodal segmentation
  • Occlusion recovery
  • Pixel embedding
  • Semantic instance segmentation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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