Boosting Segment Anything Model to Generalize Visually Non-Salient Scenarios
Unmanned System Research Institute at Northwestern Polytechnical University（西北工业大学无人系统研究院）；School of Electronic, Electrical, and Communication Engineering, University of Chinese Academic of Sciences（中国科学院大学电子电气与通信工程学院）；Max Planck Institute for Informatics (MPI-INF)（马克斯·普朗克研究所（信息研究所））；University of Science and Technology of China（中国科学技术大学）
Segment Anything Model (SAM), known for its remarkable zero-shot segmentation capabilities, has garnered significant attention in the community. Nevertheless, its performance is challenged when dealing with what we refer to as visually non-salient scenarios, where there is low contrast between the foreground and background. In these cases, existing methods often cannot capture accurate contours and fail to produce promising segmentation results. In this paper, we propose Visually Non-Salient SAM (VNS-SAM), aiming to enhance SAM's perception of visually non-salient scenarios while preserving its original zero-shot generalizability. We achieve this by effectively exploiting SAM's low-level features through two designs: Mask-Edge Token Interactive decoder and Non-Salient Feature Mining module. These designs help the SAM decoder gain a deeper understanding of non-salient characteristics with only marginal parameter increments and computational requirements. The additional parameters of VNS-SAM can be optimized within 4 hours, demonstrating its feasibility and practicality. In terms of data, we established VNS-SEG, a unified dataset for various VNS scenarios, with more than 35K images, in contrast to previous single-task adaptations. It is designed to make the model learn more robust VNS features and comprehensively benchmark the model's segmentation performance and generalizability on VNS scenarios. Extensive experiments across various VNS segmentation tasks demonstrate the superior performance of VNS-SAM, particularly under zero-shot settings, highlighting its potential for broad real-world applications. Codes and datasets are publicly available at https://guangqian-guo.github.io/VNS-SAM.
HyPSAM: Hybrid Prompt-driven Segment Anything Model for RGB-Thermal Salient Object Detection
State Key Laboratory for Novel Software Technology, Nanjing University（南京大学新型软件技术国家重点实验室）；School of Information Science and Engineering, Yunnan University（云南大学信息科学与工程学院）；School of Mathematics, Southeast University（东南大学数学学院）
SAM3-UNet: Simplified Adaptation of Segment Anything Model 3
Sun Yat-sen University（中山大学）；Jiangxi Normal University（江西师范大学）；Hainan University（海南大学）；Dalian University of Technology（大连理工大学）；Chizhou University（池州学院）；Wuhan University（武汉大学）
Application of Structural Similarity Analysis of Visually Salient Areas and Hierarchical Clustering in the Screening of Similar Wireless Capsule Endoscopic Images
SAMSOD: Rethinking SAM Optimization for RGB-T Salient Object Detection
Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University（教育部智能计算与信号处理重点实验室，计算机科学与技术学院，安徽大学）
SAM2-UNet: Segment Anything 2 Makes Strong Encoder for Natural and Medical Image Segmentation
School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；School of Computer and Information Engineering, Jiangxi Normal University（江西师范大学计算机与信息工程学院）；The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））；Tianjin University（天津大学）；Monash University（墨尔本大学）；Pazhou Lab（琶洲实验室）；Smart Hospital Research Institute, Peking University Shenzhen Hospital（北京大学深圳医院智能医院研究所）
SAMSelect: A Spectral Index Search for Marine Debris Visualization using Segment Anything
Laboratory of Geo-information Science and Remote Sensing, Wageningen University（地理信息科学与遥感实验室，瓦赫宁根大学）；Fundamental AI Lab at the University of Technology Nuremberg（技术大学纽伦堡的基础人工智能实验室）
本文提出 SAMSelect 算法，利用 Segment Anything Model 在小型标注数据上搜索最佳三通道波段或光谱指数组合，以增强 Sentinel-2 海洋垃圾目视解译，并在多地场景中验证其优于文献指数。
Weakly-Supervised RGB-D Salient Object Detection via SAM-driven Pseudo Annotation and State Space Interaction-based Diffusion
School of Communication and Information Engineering, Shanghai University（上海大学通信与信息工程学院）；School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）
Reservoir-enhanced Segment Anything Model for Subsurface Diagnosis
School of Computer Science and Technology, University of Science and Technology of China（中国科学技术大学计算机科学与技术学院）；Research Institute of Mine Artificial Intelligence, Chinese Institute of Coal Science（中国煤炭科学研究院矿山人工智能研究院）
SSP-SAM: SAM with Semantic-Spatial Prompt for Referring Expression Segmentation
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）；NExT++ Lab, School of Computing, National University of Singapore（新加坡国立大学计算机学院NExT++实验室）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）
