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Driven by large-data pre-training, Segment Anything Model (SAM) has beendemonstrated as a powerful and promptable framework, revolutionizing thesegmentation models. Despite the generality, customizing SAM for specificvisual concepts without man-powered prompting is under explored, e.g.,automatically segmenting your pet dog in different images. In this paper, wepropose a training-free Personalization approach for SAM, termed as PerSAM.Given only a single image with a reference mask, PerSAM first localizes thetarget concept by a location prior, and segments it within other images orvideos via three techniques: target-guided attention, target-semanticprompting, and cascaded post-refinement. In this way, we effectively adapt SAMfor private use without any training. To further alleviate the mask ambiguity,we present an efficient one-shot fine-tuning variant, PerSAM-F. Freezing theentire SAM, we introduce two learnable weights for multi-scale masks, onlytraining 2 parameters within 10 seconds for improved performance. Todemonstrate our efficacy, we construct a new segmentation dataset, PerSeg, forpersonalized evaluation, and test our methods on video object segmentation withcompetitive performance. Besides, our approach can also enhance DreamBooth topersonalize Stable Diffusion for text-to-image generation, which discards thebackground disturbance for better target appearance learning. Code is releasedat https://github.com/ZrrSkywalker/Personalize-SAM

Personalize Segment Anything Model with One Shot