Segment Anything Model (SAM) has shown impressive zero-shot transfer performance for various computer vision tasks recently. However, its heavy computation costs remain daunting for practical applications. MobileSAM proposes to replace the heavyweight image encoder in SAM with TinyViT by employing distillation, which results in a significant reduction in computational requirements. However, its deployment on resource-constrained mobile devices still encounters challenges due to the substantial memory and computational overhead caused by self-attention mechanisms. Recently, RepViT achieves the state-of-the-art performance and latency trade-off on mobile devices by incorporating efficient architectural designs of ViTs into CNNs. Here, to achieve real-time segmenting anything on mobile devices, following MobileSAM, we replace the heavyweight image encoder in SAM with RepViT model, ending up with the RepViT-SAM model. Extensive experiments show that RepViT-SAM can enjoy significantly better zero-shot transfer capability than MobileSAM, along with nearly $10\times$ faster inference speed. The code and models are available at \url{https://github.com/THU-MIG/RepViT}.
Detecting and segmenting individual objects, regardless of their category, is crucial for many applications such as action detection or robotic interaction. While this problem has been well-studied under the classic formulation of spatio-temporal grouping, state-of-the-art approaches do not make use of learning-based methods. To bridge this gap, we propose a simple learning-based approach for spatio-temporal grouping. Our approach leverages motion cues from optical flow as a bottom-up signal for separating objects from each other. Motion cues are then combined with appearance cues that provide…
Multi-scale Contrastive Adaptor Learning for Segmenting Anything in Underperformed Scenes
School of Automation and Electrical Engineering, University of Science and Technology Beijing（北京科技大学自动化与电气工程学院）；Alibaba DAMO Academy（阿里巴巴达摩院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）
RefSAM: Efficiently Adapting Segmenting Anything Model for Referring Video Object Segmentation
College of Computer Science and Technology, National University of Defense Technology（国防科技大学计算机学院）；School of Computer Science, The University of Sydney（悉尼大学计算机学院）
Can SAM Count Anything? An Empirical Study on SAM Counting
Shenzhen Institute of Advanced Technology, Chinese Academy of Science（中国科学院深圳先进技术研究院）；Harbin Institute of Technology（哈尔滨工业大学）；Peng Cheng Laboratory（鹏城实验室）
本文实证评估 SAM 在少样本目标计数任务上的能力，发现未经微调的 SAM 相比现有方法表现不佳，尤其难以处理小型和拥挤目标。
NTO3D: Neural Target Object 3D Reconstruction with Segment Anything
University of Chinese Academy of Sciences（中国科学院大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Intel Labs China（英特尔中国实验室）；Peking University（北京大学）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）
A Comprehensive Survey on Segment Anything Model for Vision and Beyond
Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Cooperative Medianet Innovation Center, Shanghai Jiao Tong University（上海交通大学媒体网络协同创新中心）；CloudWalk Technology Co., Ltd（云从科技有限公司）；University of Oulu（奥卢大学）；Northwestern Polytechnical University（西北工业大学）；Central South University（中南大学）
SAM3D: Segment Anything Model in Volumetric Medical Images
University of Arkansas（阿肯色大学）；University of Science, Vietnam National University（越南国家大学科学大学）；John von Neumann Institute, Vietnam National University（越南国家大学约翰·冯·诺依曼研究所）；West Virginia University（西弗吉尼亚大学）；University of Arkansas for Medical Sciences（阿肯色医科大学）
ViRefSAM: Visual Reference-Guided Segment Anything Model for Remote Sensing Segmentation
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院航空信息研究所）；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）；University of Chinese Academy of Sciences（中国科学院大学）；Key Laboratory of Target Cognition and Application Technology(TCAT), Aerospace Information Research Institute, Chinese Academy of Sciences（目标认知与应用技术重点实验室）；Department of Electronic Engineering, Tsinghua University（清华大学电子工程系）；School of Automation, Northwestern Polytechnical University（西北工业大学自动化学院）
The Hong Kong University of Science Technology（香港科技大学）；Huazhong University of Science Technology（华中科技大学）；Nanyang Technological University（南洋理工大学）；Xi’an Jiaotong University（西安交通大学）；National University Singapore（新加坡国立大学）
针对 SAM 在图像分割中偏重纹理而忽视几何信息的问题，提出 SAD 模型，通过分割渲染深度图提取几何信息并缓解过度分割，结合开放词汇语义分割实现了 3D 全景分割。
Quantifying the Limits of Segmentation Foundation Models: Modeling Challenges in Segmenting Tree-Like and Low-Contrast Objects
Department of Electrical and Computer Engineering（电气与计算机工程系）；Department of Computer Science（计算机科学系）；Department of Radiology（放射学系）；Department of Biostatistics & Bioinformatics（生物统计学与生物信息学系）；Minnesota Health Solutions（明尼苏达健康解决方案）
Inspiring the Next Generation of Segment Anything Models: Comprehensively Evaluate SAM and SAM 2 with Diverse Prompts Towards Context-Dependent Concepts under Different Scenes
Yale University, USA（耶鲁大学）；Nanyang Technological University, Singapore（南洋理工大学）；Dalian University of Technology, China（大连理工大学）；X3000 Inspection Co., Ltd, China（X3000检测有限公司）
CUHK（香港中文大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Peking University（北京大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Comparative Analysis of Segment Anything Model and U-Net for Breast Tumor Detection in Ultrasound and Mammography Images
Florida Atlantic University（佛罗里达大西洋大学）；University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校）；Shahid Beheshti University of Medical Sciences（沙希德·贝赫什提医科大学）；Missouri University of Science and Technology（密苏里科技大学）；Florida International University（佛罗里达国际大学）
School of Artificial Intelligence, Nanjing University（南京大学人工智能学院）；Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University（南京大学医学院附属金陵医院）
Can SAM recognize crops? Quantifying the zero-shot performance of a semantic segmentation foundation model on generating crop-type maps using satellite imagery for precision agriculture
GlanceSeg: Real-time microaneurysm lesion segmentation with gaze-map-guided foundation model for early detection of diabetic retinopathy
Southern University of Science and Technology（南方科技大学）；Wenzhou Medical University（温州医科大学）；Intelligent Vision Plus Technology Co., Ltd.（深圳市智能视觉加科技有限公司）；The Chinese University of Hong Kong（香港中文大学）
