Segment objects using semantic segmentation masks without additional manual labeling with TFISeg
Segment objects using semantic segmentation masks without additional manual labeling with TFISeg
Training-Free 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
… training of a fully-supervised instance segmentation model requires costly both instance-level and pixel-level annotations.
… propose a novel paradigm for instance segmentation called training-free instance segmentation (TFISeg), which achieves instance segmentation results from image masks predicted using off-the-shelf semantic segmentation models.
TFISeg does not require training a semantic or/and instance segmentation model and avoids the need for instance-level image annotations. Therefore, it is highly efficient.
… 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
… Finally, instance segmentation results are obtained after being refined by a learnable category-agnostic object boundary branch.
… demonstrate that TFISeg 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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