The Segment Anything Model (SAM) has demonstrated impressive generalization in prompt-based segmentation. Yet, the potential of semantic text prompts remains underexplored compared to traditional spatial prompts like points and boxes. This paper introduces SAM-PTx, a parameter-efficient approach for adapting SAM using frozen CLIP-derived text embeddings as class-level semantic guidance. Specifically, we propose a lightweight adapter design called Parallel-Text that injects text embeddings into SAM's image encoder, enabling semantics-guided segmentation while keeping most of the original architecture frozen. Our adapter modifies only the MLP-parallel branch of each transformer block, preserving the attention pathway for spatial reasoning. Through supervised experiments and ablations on the COD10K dataset as well as low-data subsets of COCO and ADE20K, we show that incorporating fixed text embeddings as input improves segmentation performance over purely spatial prompt baselines. To our knowledge, this is the first work to use text prompts for segmentation on the COD10K dataset. These results suggest that integrating semantic conditioning into SAM's architecture offers a practical and scalable path for efficient adaptation with minimal computational complexity.
SAM3-Adapter: Efficient Adaptation of Segment Anything 3 for Camouflage Object Segmentation, Shadow Detection, and Medical Image Segmentation
KOKONI, Moxin (Huzhou) Tech. Co., LTD（摩西（湖州）科技有限公司）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；School of Information Engineering, Huzhou University（湖州大学信息工程学院）；School of Electrical and Electronic Engineering, Nanyang Technological University（新加坡南洋理工大学电子与电气工程学院）；College of Computing and Data Science, Nanyang Technological University（新加坡南洋理工大学计算与数据科学学院）；School of Information Science and Technology, University of Science and Technology of China（中国科学技术大学信息科学与技术学院）
SAM2-Adapter: Evaluating & Adapting Segment Anything 2 in Downstream Tasks: Camouflage, Shadow, Medical Image Segmentation, and More
KOKONI, Moxin (Huzhou) Tech. Co., LTD（科科尼（湖州）魔芯科技有限公司）；School of Information Engineering, Huzhou University（湖州大学信息工程学院）；School of Information Science and Technology, University of Science and Technology of China（中国科学技术大学信息科学技术学院）；School of Software Technology, Zhejiang University（浙江大学软件学院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Information Systems Technology and Design Pillar, Singapore University of Technology and Design（新加坡科技设计大学信息系统技术与设计支柱学院）
SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and More
KOKONI, Moxin (Huzhou) Tech. Co., LTD（莫西（湖州）科技有限公司KOKONI）；Beihang University（北京航空航天大学）；Zhejiang University（浙江大学）；Huzhou University（湖州师范学院）；Singapore University of Technology and Design（新加坡科技设计大学）
Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation
University of Oxford（牛津大学）；National University of Singapore（新加坡国立大学）；University of Alberta（阿尔伯塔大学）；A*STAR（新加坡科技研究局）；Carnegie Mellon University（卡内基梅隆大学）；Singapore Eye Research Institute（新加坡眼科研究所）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；University of Hongkong（香港大学）
本文提出Medical SAM Adapter（Med-SA），通过轻量适配技术将医学知识融入SAM，利用SD-Trans和HyP-Adpt适配2D模型至3D医学图像，在17个任务上以仅2%参数更新超越SOTA方法。
BIR-Adapter: A parameter-efficient diffusion adapter for blind image restoration
Chair of Media Technology, Munich Institute of Robotics and Machine Intelligence（媒体技术教授会，慕尼黑机器人与机器智能研究所）；School of Computation, Information and Technology, Technical University of Munich（计算、信息与技术学院，慕尼黑技术大学）
Continual Adapter Tuning with Semantic Shift Compensation for Class-Incremental Learning
HUST AI & Visual Learning Lab (HAIV Lab)（华中科技大学人工智能与视觉学习实验室）；Huazhong University of Science and Technology (HUST)（华中科技大学）；Department of Computer Science（计算机科学系）；Boston University（波士顿大学）；ByteDance, Inc.（字节跳动公司）
Spectral Adapters for Segment Anything Model-based Segmentation of Colorectal Liver Metastases in Computed Tomography
Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心）；University of Alberta（阿尔伯塔大学）；Alberta Machine Intelligence Institute（阿尔伯塔机器智能研究所）；The University of Texas MD Anderson Cancer Center（德克萨斯大学MD安德森癌症中心）；Toronto General Hospital Research Institute, University Health Network（大学健康网络多伦多总医院研究所）
Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter
Anhui Provincial Key Laboratory of Multimodal Cognitive Computation（安徽省级多模态认知计算重点实验室）；School of Computer Science and Technology（计算机科学与技术学院）；Anhui University（安徽大学）
The Chinese University of Hong Kong（香港中文大学）；University of Surrey（萨里大学）；Centre for Perceptual and Interactive Intelligence Limited（感知与交互智能中心有限公司）
Capitalizing on large pre-trained models for various downstream tasks of interest have recently emerged with promising performance. Due to the ever-growing model size, the standard full fine-tuning based task adaptation strategy becomes prohibitively costly in terms of model training and storage. This has led to a new research direction in parameter-efficient transfer learning. However, existing attempts typically focus on downstream tasks from the same modality (e.g., image understanding) of the pre-trained model. This creates a limit because in some specific modalities, (e.g., video understa…
Fine-Grained VLM Fine-tuning via Latent Hierarchical Adapter Learning
Information Materials and Intelligent Sensing Laboratory of Anhui Province（安徽省信息材料与智能感知实验室）；Anhui Provincial Key Laboratory of Multimodal Cognitive Computation（安徽省多模态认知计算重点实验室）；School of Computer Science and Technology, Anhui University（安徽大学计算机科学与技术学院）
Test-time Alignment-Enhanced Adapter for Vision-Language Models
school of computer science；engineering sun yat-sen university Guangzhou, China；school of artificial intelligence sun yat-sen university Guangzhou, China
UrbanSAM: Learning Invariance-Inspired Adapters for Segment Anything Models in Urban Construction
School of Mathematics and Statistics, Southeast University（东南大学数学与统计学院）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences（中国科学院大学电子电气与通信工程学院）；College of Resources and Environment, University of Chinese Academy of Sciences（中国科学院大学资源与环境学院）；Image Processing Laboratory (IPL), Universitat de València（瓦伦西亚大学图像处理实验室）；Data Science in Earth Observation, Technical University of Munich（慕尼黑工业大学地球观测数据科学研究所）；Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK（格勒诺布尔阿尔卑斯大学、法国国家信息与自动化研究所、法国国家科学研究中心、格勒诺布尔理工学院、约瑟夫·傅里叶实验室）
SenseTime（商汤科技）；School of Electronic and Electrical Engineering, Shanghai University of Engineering Science（上海工程技术大学电子与电气工程学院）；School of Computer Science and Technology, Donghua University（东华大学计算机科学与技术学院）
IPVTON: Image-based 3D Virtual Try-on with Image Prompt Adapter
School of Software Engineering, South China University of Technology（华南理工大学软件学院）；Nanyang Technological University（南洋理工大学）；SenseTime Research（商汤科技研究院）；Peng Cheng Laboratory（鹏城实验室）
Large-scale pretraining is fast becoming the norm in Vision-Language (VL) modeling. However, prevailing VL approaches are limited by the requirement for labeled data and the use of complex multi-step pretraining objectives. We present MAGMA - a simple method for augmenting generative language models with additional modalities using adapter-based finetuning. Building on Frozen, we train a series of VL models that autoregressively generate text from arbitrary combinations of visual and textual input. The pretraining is entirely end-to-end using a single language modeling objective, simplifying o…
Fine-Grained Scene Image Classification with Modality-Agnostic Adapter
School of Computer Science, Fudan University（复旦大学计算机科学学院）；Innovation Center of Calligraphy and Painting Creation Technology, MCT（中国传媒书画创作技术创新中心）；Videt Technology（维德科技）
