Segment objects in scene after training only on object types with SISeg
Segment objects in scene after training only on object types with SISeg
Synthetic Instance Segmentation from Semantic Image Segmentation Masks
arXiv paper abstract https://arxiv.org/abs/2308.00949
arXiv PDF paper https://arxiv.org/pdf/2308.00949.pdf
... the training of a fully-supervised instance segmentation model requires costly both instance-level and pixel-level annotations.
... propose ... synthetic instance segmentation (SISeg) ... from image masks predicted using off-the-shelf semantic segmentation models ... and avoids the need for instance-level image annotations.
... first obtain a semantic segmentation mask of the input image via a trained semantic segmentation model.
... calculate a displacement field vector for each pixel based on the segmentation mask, which can indicate representations belonging to the same class but different instances, i.e., obtaining the instance-level object information.
Finally, instance segmentation results are obtained after being refined by a learnable category-agnostic object boundary branch.
... SISeg can achieve competitive results compared to the state-of-the-art fully-supervised instance segmentation methods without the need for additional human resources or increased computational costs ...
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