SPDA-SAM: A Self-prompted Depth-Aware Segment Anything Model for Instance Segmentation
State Key Laboratory of Physical Oceanography and the Faculty of Information Science and Engineering, Ocean University of China（物理海洋学国家重点实验室和中国海洋大学信息科学与工程学院）；School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区计算机科学与技术学院）
Recently, Segment Anything Model (SAM) has demonstrated strong generalizability in various instance segmentation tasks. However, its performance is severely dependent on the quality of manual prompts. In addition, the RGB images that instance segmentation methods normally use inherently lack depth information. As a result, the ability of these methods to perceive spatial structures and delineate object boundaries is hindered. To address these challenges, we propose a Self-prompted Depth-Aware SAM (SPDA-SAM) for instance segmentation. Specifically, we design a Semantic-Spatial Self-prompt Module (SSSPM) which extracts the semantic and spatial prompts from the image encoder and the mask decoder of SAM, respectively. Furthermore, we introduce a Coarse-to-Fine RGB-D Fusion Module (C2FFM), in which the features extracted from a monocular RGB image and the depth map estimated from it are fused. In particular, the structural information in the depth map is used to provide coarse-grained guidance to feature fusion, while local variations in depth are encoded in order to fuse fine-grained feature representations. To our knowledge, SAM has not been explored in such self-prompted and depth-aware manners. Experimental results demonstrate that our SPDA-SAM outperforms its state-of-the-art counterparts across twelve different data sets. These promising results should be due to the guidance of the self-prompts and the compensation for the spatial information loss by the coarse-to-fine RGB-D fusion operation.
HFP-SAM: Hierarchical Frequency Prompted SAM for Efficient Marine Animal Segmentation
School of Future Technology（未来技术学院）；Central Hospital of Dalian University of Technology（大连理工大学附属中心医院）；Affiliated Zhongshan Hospital of Dalian University（大连大学附属中山医院）；School of Information and Communication Engineering（信息与通信工程学院）
Segment Anything in Pathology Images with Natural Language
Department of Computer Science and Engineering（计算机科学与工程系）；The Hong Kong University of Science and Technology（香港科学与技术大学）；School of Electronic Engineering and Information Science（电子工程与信息科学学院）；University of Science and Technology of China（中国科学技术大学）；The Chinese University of Hong Kong（香港中文大学）；Tencent AI Platform Department（腾讯人工智能平台部门）；Department of Anatomical and Cellular Pathology（解剖与细胞病理学部）；Department of Chemical and Biological Engineering（化学与生物工程系）；Division of Life Science（生命科学系）；HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute（香港科技大学深圳-香港协同创新研究院）；State Key Laboratory of Nervous System Disorders（神经系统疾病国家重点实验室）
S-SAM: SVD-based Fine-Tuning of Segment Anything Model for Medical Image Segmentation
The Johns Hopkins University（约翰斯·霍普金斯大学）；Wilmer Eye Institute, The Johns Hopkins University（约翰斯·霍普金斯大学威尔默眼科研究所）；Malone Center for Engineering in Healthcare, The Johns Hopkins University（约翰斯·霍普金斯大学马龙医疗保健工程中心）；Department of Electrical and Computer Engineering, The Johns Hopkins University（约翰斯·霍普金斯大学电气与计算机工程系）
Part-aware Prompted Segment Anything Model for Adaptive Segmentation
Department of Computer Science and Engineering, University of Michigan（计算机科学与工程系，密歇根大学）；Department of Electrical and Computer Engineering, University of Michigan（电气与计算机工程系，密歇根大学）
DeSAM: Decoupled Segment Anything Model for Generalizable Medical Image Segmentation
Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences（中国科学院苏州生物医学工程技术研究所）；The Hong Kong Polytechnic University（香港理工大学）
Unleashing the Power of Prompt-driven Nucleus Instance Segmentation
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Engineering, Westlake University（西湖大学工学院）；University of Liverpool（利物浦大学）
Test-Time Adaptation with SaLIP: A Cascade of SAM and CLIP for Zero shot Medical Image Segmentation
ML-Labs, Dublin City University（都柏林城市大学 ML-Labs）；ML-Labs, University College Dublin（都柏林大学 ML-Labs）；Centre for Applied AI (CeADAR), University College Dublin（都柏林大学应用人工智能中心 (CeADAR)）；Insight SFI Centre for Data Analytics, Dublin City University（都柏林城市大学 Insight SFI 数据分析中心）
SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation
School of Electronic and Information Engineering, South China University of Technology（华南理工大学电子与信息工程学院）；Pazhou Lab（琶洲实验室）；College of Electronic and Information Engineering, Tongji University（同济大学电子与信息工程学院）
CycleSAM: Few-Shot Surgical Scene Segmentation with Cycle- and Scene-Consistent Feature Matching
University of Strasbourg, CNRS, INSERM, ICube, UMR7357（斯特拉斯堡大学、国家科学研究中心、法国国家医学研究院、ICube、UMR7357）；IHU Strasbourg（斯特拉斯堡IHU）；Fondazione Policlinico Universitario A. Gemelli IRCCS（阿·吉梅利大学附属医院基金会）
Joint-Optimized Unsupervised Adversarial Domain Adaptation in Remote Sensing Segmentation with Prompted Foundation Model
School of Electronics and Information Engineering, Beihang University（北京航空航天大学电子信息工程学院）；University of Liverpool（利物浦大学）；Image Processing Center, School of Astronautics, Beihang University（北京航空航天大学宇航学院图像处理中心）
APSeg: Auto-Prompt Network for Cross-Domain Few-Shot Semantic Segmentation
Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳市人工智能与机器人研究院）；National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University（深圳大学大数据系统计算技术国家工程实验室）；School of Computer Science, University of Nottingham, China（诺丁汉大学计算机学院（中国））
SUGFW+: An Uncertainty-guided Feature Weighting Framework for Cold Start Active Adaptation of SAM in Medical Image Segmentation
University of Electronic Science and Technology of China（电子科技大学）；Glassgow College, University of Electronic Science and Technology of China（电子科技大学格拉斯哥学院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；West China Hospital, Sichuan University（四川大学华西医院）
Prompt-Driven Lightweight Foundation Model for Instance Segmentation-Based Fault Detection in Freight Trains
School of Mechanical Engineering, Hubei University of Technology（湖北工业大学机械工程学院）；Hubei Key Laboratory of Modern Manufacturing Quality Engineering, Hubei University of Technology（湖北工业大学现代制造质量工程重点实验室）；School of Mechanical Science and Engineering, Huazhong University of Science and Technology（华中科技大学机械科学与工程学院）
SAM Meets Robotic Surgery: An Empirical Study in Robustness Perspective
The Chinese University of Hong Kong（香港中文大学）；University College London（伦敦大学学院）；National University of Singapore（新加坡国立大学）；Hubei University of Technology（湖北工业大学）
SAM Meets Robotic Surgery: An Empirical Study on Generalization, Robustness and Adaptation
University College London（伦敦大学学院）；National University of Singapore（新加坡国立大学）；Hubei University of Technology（湖北工业大学）；The Chinese University of Hong Kong（香港中文大学）
本文在 EndoVis 2017/2018 上实证评估 SAM 的手术器械零样本分割、扰动鲁棒性与提示表现，并通过 LoRA 微调提出无需提示的 SurgicalSAM。
Wetland mapping from sparse annotations with satellite image time series and temporal-aware segment anything model
Department of Geography, The University of Hong Kong, Hong Kong, China（香港大学地理系）；Pengcheng Laboratory, Shenzhen, China（深圳鹏城实验室）；Department of Electronics and Information Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, China（哈尔滨工业大学（深圳）电子与信息工程学院）；Key Laboratory for Geo-Environmental Monitoring of Great Bay Area, Ministry of Natural Resources, Guangdong Key Laboratory of Urban Informatics, Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen, China（粤港澳大湾区地理环境监测重点实验室，自然资源部，广东省城市信息学重点实验室，深圳空间智能感知与服务重点实验室，深圳大学）
TP-DRSeg: Improving Diabetic Retinopathy Lesion Segmentation with Explicit Text-Prompts Assisted SAM
Monash-Airdoc Research（莫纳什-鹰瞳研究中心）；Monash University（莫纳什大学）；Monash Medical AI Group（莫纳什医学人工智能集团）；School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）
AdaptiveSAM: Towards Efficient Tuning of SAM for Surgical Scene Segmentation
Johns Hopkins University（约翰斯·霍普金斯大学）；Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院）；Wilmer Eye Institute（威尔默眼科研究所）；Malone Center for Engineering in Healthcare（马龙医疗工程中心）
