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.
CITATION STYLE
Liu, Y., Psota, E. T., & Pérez, L. C. (2019). Layered embeddings for amodal instance segmentation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11662 LNCS, pp. 102–111). Springer Verlag. https://doi.org/10.1007/978-3-030-27202-9_9
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