@inproceedings{ce68e4f642ba4aa2bd7583409b0ad434,
title = "Layered embeddings for amodal instance segmentation",
abstract = "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 https://github.com/yanfengliu/layered_embeddings.",
keywords = "Amodal segmentation, Occlusion recovery, Pixel embedding, Semantic instance segmentation",
author = "Yanfeng Liu and Psota, {Eric T.} and P{\'e}rez, {Lance C.}",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 16th International Conference on Image Analysis and Recognition, ICIAR 2019 ; Conference date: 27-08-2019 Through 29-08-2019",
year = "2019",
doi = "10.1007/978-3-030-27202-9_9",
language = "English (US)",
isbn = "9783030272012",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "102--111",
editor = "Fakhri Karray and Alfred Yu and Aur{\'e}lio Campilho",
booktitle = "Image Analysis and Recognition - 16th International Conference, ICIAR 2019, Proceedings",
}