ASAM: Boosting Segment Anything Model with Adversarial Tuning
CVPR 2024(2024)
摘要
In the evolving landscape of computer vision, foundation models have emerged
as pivotal tools, exhibiting exceptional adaptability to a myriad of tasks.
Among these, the Segment Anything Model (SAM) by Meta AI has distinguished
itself in image segmentation. However, SAM, like its counterparts, encounters
limitations in specific niche applications, prompting a quest for enhancement
strategies that do not compromise its inherent capabilities. This paper
introduces ASAM, a novel methodology that amplifies SAM's performance through
adversarial tuning. We harness the potential of natural adversarial examples,
inspired by their successful implementation in natural language processing. By
utilizing a stable diffusion model, we augment a subset (1
dataset, generating adversarial instances that are more representative of
natural variations rather than conventional imperceptible perturbations. Our
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. The results of our extensive
evaluations confirm that ASAM establishes new benchmarks in segmentation tasks,
thereby contributing to the advancement of foundational models in computer
vision. Our project page is in https://asam2024.github.io/.
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