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arXiv Computer Vision By Christian Wilms, Emre G\"ulsoylu, Sven Schreiber, Simone Frintrop

MixSIS: Mixed-supervised instance segmentation

Read the original on arXiv Computer Vision →

MixSIS introduces a mixed-supervised instance segmentation framework that permits a flexible combination of image, point, box, and mask annotations within a dataset, addressing the limitation of existing weakly-supervised methods that focus on a single annotation level. The authors present AnnsMixer, a baseline system that generates pseudo mask annotations from all annotation levels, even within a single image, and demonstrate its effectiveness on both WSIS and MixSIS tasks. Experiments and case studies show that MixSIS and AnnsMixer can reduce annotation time by up to 90% or improve segmentation results by 34% using simple annotation rules or heterogeneous data.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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