Segment object using stable diffusion to fine-tune SAM with ASAM
Segment object using stable diffusion to fine-tune SAM with ASAM
ASAM: Boosting Segment Anything Model with Adversarial Tuning
arXiv paper abstract https://arxiv.org/abs/2405.00256
arXiv PDF paper https://arxiv.org/pdf/2405.00256
Hugging Face https://huggingface.co/spaces/xhk/ASAM
Project page https://asam2024.github.io
... Segment Anything Model (SAM) by Meta AI has distinguished itself in image segmentation.
... This paper introduces ASAM, a novel methodology that amplifies SAM's performance through adversarial tuning.
... By utilizing a stable diffusion model, ... augment a subset (1%) of the SA-1B dataset, generating adversarial instances that are more representative of natural variations rather than conventional imperceptible perturbations.
... approach maintains the photorealism of adversarial examples and ensures alignment with original mask annotations, thereby preserving the integrity of the segmentation task.
The fine-tuned ASAM demonstrates significant improvements across a diverse range of segmentation tasks without necessitating additional data or architectural modifications.
... evaluations confirm that ASAM establishes new benchmarks in segmentation tasks, thereby contributing to the advancement of foundational models in computer vision ...
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