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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