PA-SAM: Prompt Adapter SAM for High-Quality Image Segmentation
The Segment Anything Model (SAM) has exhibited outstanding performance in various image segmentation tasks. Despite being trained with over a billion masks, SAM faces challenges in mask prediction quality in numerous scenarios, especially in real-world contexts. In this paper, we introduce a novel prompt-driven adapter into SAM, namely Prompt Adapter Segment Anything Model (PA-SAM), aiming to enhance the segmentation mask quality of the original SAM. By exclusively training the prompt adapter, PA-SAM extracts detailed information from images and optimizes the mask decoder feature at both sparse and dense prompt levels, improving the segmentation performance of SAM to produce high-quality masks. Experimental results demonstrate that our PA-SAM outperforms other SAM-based methods in high-quality, zero-shot, and open-set segmentation. We're making the source code and models available at https://github.com/xzz2/pa-sam.
Code (1)
Tasks
DecoderImage SegmentationSegmentationSemantic SegmentationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation
The Segment Anything Model (SAM) has recently gained popularity in the field of image segmentation due to its impressive capabilities in various segmentation tasks and its prompt-based interface. However, recent studies …
Image SegmentationMedical Image SegmentationSegmentationSemantic SegmentationSelf-Prompt SAM: Medical Image Segmentation via Automatic Prompt SAM Adaptation
Segment Anything Model (SAM) has demonstrated impressive zero-shot performance and brought a range of unexplored capabilities to natural image segmentation tasks. However, as a very important branch of image segmentation…
Image SegmentationMedical Image SegmentationSegmentationSemantic SegmentationTuning a SAM-Based Model with Multi-Cognitive Visual Adapter to Remote Sensing Instance Segmentation
The Segment Anything Model (SAM), a foundational model designed for promptable segmentation tasks, demonstrates exceptional generalization capabilities, making it highly promising for natural scene image segmentation. Ho…
DecoderImage SegmentationInstance SegmentationSegmentation+2SUR-adapter: Enhancing Text-to-Image Pre-trained Diffusion Models with Large Language Models
Diffusion models, which have emerged to become popular text-to-image generation models, can produce high-quality and content-rich images guided by textual prompts. However, there are limitations to semantic understanding…
Image GenerationKnowledge Distillationparameter-efficient fine-tuningText to Image Generation+1Prompt-Free and Efficient SAM2 Adaptation for Biomedical Semantic Segmentation via Dual Adapters
Segment Anything Model 2 (SAM2) demonstrated impressive zero-shot capabilities on natural images but faces challenges in biomedical segmentation due to significant domain shifts and prompt dependency. To address these li…
parameter-efficient fine-tuningSemantic Segmentation