# PA-SAM: Prompt Adapter SAM for High-Quality Image Segmentation

arXiv: https://arxiv.org/abs/2401.13051 · HTML: https://mlsanity.com/paper/2401.13051/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 26 references that are in the map.
- First posted on arXiv 23 Jan 2024.

## Builds on (most cited first, 21 of 21)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection](https://mlsanity.com/paper/2203.03605/) (2022)
- [Segment Anything in High Quality](https://mlsanity.com/paper/2306.01567/) (2023)
- [Personalize Segment Anything Model with One Shot](https://mlsanity.com/paper/2305.03048/) (2023)
- [Inpaint Anything: Segment Anything Meets Image Inpainting](https://mlsanity.com/paper/2304.06790/) (2023)
- [RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Model](https://mlsanity.com/paper/2306.16269/) (2023)
- [AIM: Adapting Image Models for Efficient Video Action Recognition](https://mlsanity.com/paper/2302.03024/) (2023)
- [Parameter-Efficient Orthogonal Finetuning via Butterfly Factorization](https://mlsanity.com/paper/2311.06243/) (2023)
- [Highly Accurate Dichotomous Image Segmentation](https://mlsanity.com/paper/2203.03041/) (2022)
- [SurgicalSAM: Efficient Class Promptable Surgical Instrument Segmentation](https://mlsanity.com/paper/2308.08746/) (2023)
- [Towards High-Resolution Salient Object Detection](https://mlsanity.com/paper/1908.07274/) (2019)
- [Matte Anything: Interactive Natural Image Matting with Segment Anything Models](https://mlsanity.com/paper/2306.04121/) (2023)
- [SAMAug: Point Prompt Augmentation for Segment Anything Model](https://mlsanity.com/paper/2307.01187/) (2023)
- [Can SAM Boost Video Super-Resolution?](https://mlsanity.com/paper/2305.06524/) (2023)
- [FoodSAM: Any Food Segmentation](https://mlsanity.com/paper/2308.05938/) (2023)
- [Let Segment Anything Help Image Dehaze](https://mlsanity.com/paper/2306.15870/) (2023)

## Built on by (most cited first, 17 of 17)

- [Cross-Modal Bidirectional Interaction Model for Referring Remote Sensing Image Segmentation](https://mlsanity.com/paper/2410.08613/) (2024)
- [Adapting Pre-Trained Vision Models for Novel Instance Detection and Segmentation](https://mlsanity.com/paper/2405.17859/) (2024)
- [ROS-SAM: High-Quality Interactive Segmentation for Remote Sensing Moving Object](https://mlsanity.com/paper/2503.12006/) (2025)
- [CRISP-SAM2: SAM2 with Cross-Modal Interaction and Semantic Prompting for Multi-Organ Segmentation](https://mlsanity.com/paper/2506.23121/) (2025)
- [Unveiling the Potential of Segment Anything Model 2 for RGB-Thermal Semantic Segmentation with Language Guidance](https://mlsanity.com/paper/2503.02581/) (2025)
- [RClicks: Realistic Click Simulation for Benchmarking Interactive Segmentation](https://mlsanity.com/paper/2410.11722/) (2024)
- [EchoONE: Segmenting Multiple echocardiography Planes in One Model](https://mlsanity.com/paper/2412.02993/) (2024)
- [SAM-REF: Introducing Image-Prompt Synergy during Interaction for Detail Enhancement in the Segment Anything Model](https://mlsanity.com/paper/2408.11535/) (2024)
- [Segment Anything for Video: A Comprehensive Review of Video Object Segmentation and Tracking from Past to Future](https://mlsanity.com/paper/2507.22792/) (2025)
- [Foundation Models for Amodal Video Instance Segmentation in Automated Driving](https://mlsanity.com/paper/2409.14095/) (2024)
- [PolSAM: Polarimetric Scattering Mechanism Informed Segment Anything Model](https://mlsanity.com/paper/2412.12737/) (2024)
- [WRT-SAM: Foundation Model-Driven Segmentation for Generalized Weld Radiographic Testing](https://mlsanity.com/paper/2502.11338/) (2025)
- [Adapting Segment Anything Model for Power Transmission Corridor Hazard Segmentation](https://mlsanity.com/paper/2505.22105/) (2025)
- [Towards Integrating Uncertainty for Domain-Agnostic Segmentation](https://mlsanity.com/paper/2512.23427/) (2025)
- [IP-SAM: Rethinking Prompt-Conditioned Segmentation for Prompt-Absent Deployment](https://mlsanity.com/paper/2603.27250/) (2026)
- [Image-Conditioned Instance Prompt Network for Referring Remote Sensing Image Segmentation](https://mlsanity.com/paper/2605.24532/) (2026)
- [Enhancing MedSAM with a Lightweight Box Predictor for Medical Image Segmentation](https://mlsanity.com/paper/2606.04705/) (2026)

## Abstract (arXiv)

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.