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Segment object using one example without training using target-guided attention with PerSAM

Segment object using one example without training using target-guided attention with PerSAM


Personalize Segment Anything Model with One Shot

arXiv paper abstract https://arxiv.org/abs/2305.03048



Driven by large-data pre-training, Segment Anything Model (SAM) has been demonstrated as a powerful and promptable framework, revolutionizing the segmentation models.


Despite the generality, customizing SAM for specific visual concepts without man-powered prompting is under explored, e.g., automatically segmenting your pet dog in different images.


... propose a training-free Personalization approach for SAM, termed as PerSAM.


Given only a single image with a reference mask, PerSAM first localizes the target concept by a location prior, and segments it within other images or videos via three techniques: target-guided attention, target-semantic prompting, and cascaded post-refinement.


... further ... present an efficient one-shot fine-tuning variant, PerSAM-F.


Freezing the entire SAM, ... introduce two learnable weights for multi-scale masks, only training 2 parameters within 10 seconds for improved performance ...



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