Judging from Support-set: A New Way to Utilize Few-Shot Segmentation for Segmentation Refinement Process
Brookhaven National Laboratory（布鲁赫斯国家实验室）；Roblox（Roblox公司）；Rutgers University（罗格斯大学）；Mohamed Bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Seonghyeon Moon, Qingze, Liu, Haein Kong, Muhammad Haris Khan
分割细化旨在增强由分割算法生成的初始粗略掩码。细化后的掩码被期望能捕捉目标对象的更多细节和更好的轮廓。分割细化的研究是作为对高质量图像分割需求的响应而发展起来的。然而，据我们所知，目前尚未开发出能够判定分割细化是否成功的方法。这种方法可以确保在分割结果至关重要的应用中分割的可靠性，并促进图像处理技术的创新。为了填补这一研究空白，我们提出了一种名为基于支持集的判断（Judging From Support-set, JFS）的方法，该方法利用现成的少样本分割（FSS）模型来判定分割细化的成功与否。FSS中问题的传统目标是利用支持集提供的目标信息在查询图像中找到目标对象。然而，我们在分割细化方法的评估流程中提出了一种FSS模型的新型应用。给定粗略掩码作为输入，分割细化方法生成细化掩码；这两个掩码成为FSS模型的新支持掩码。现有的支持掩码随后作为FSS模型的测试集，以评估分割细化方法所生成细化分割的质量。我们通过在PASCAL数据集上使用SegGPT作为FSS模型的选择来评估SAM增强伪标签（SEPL），展示了我们提出的JFS框架的有效性。结果表明，JFS具有判定分割细化过程是否成功的潜力。
英文摘要：
Segmentation refinement aims to enhance the initial coarse masks generated by segmentation algorithms. The refined masks are expected to capture more details and better contours of the target objects. Research on segmentation refinement has developed as a response to the need for high-quality image segmentations. However, to our knowledge, no method has been developed that can determine the success of segmentation refinement. Such a method could ensure the reliability of segmentation in applications where the outcome of the segmentation is important and fosters innovation in image processing technologies. To address this research gap, we propose Judging From Support-set (JFS), a method to judge the success of segmentation refinement leveraging an off-the-shelf few-shot segmentation (FSS) model. The traditional goal of the problem in FSS is to find a target object in a query image utilizing target information given by a support set. However, we propose a novel application of the FSS model in our evaluation pipeline for segmentation refinement methods. Given a coarse mask as input, segmentation refinement methods produce a refined mask; these two masks become new support masks for the FSS model. The existing support mask then serves as the test set for the FSS model to evaluate the quality of the refined segmentation by the segmentation refinement methods. We demonstrate the effectiveness of our proposed JFS framework by evaluating the SAM Enhanced Pseudo-Labels (SEPL) using SegGPT as the choice of FSS model on the PASCAL dataset. The results showed that JFS has the potential to determine whether the segmentation refinement process is successful.
iSeg: An Iterative Refinement-based Framework for Training-free Segmentation
School of Electrical and Information Engineering, Tianjin University（天津大学电气与信息工程学院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；School of Big Data and Software Engineering, Chongqing University（重庆大学大数据与软件工程学院）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Linkoping University（林雪平大学）
CIR-Net: Cross-modality Interaction and Refinement for RGB-D Salient Object Detection
Beijing Jiaotong University（北京交通大学）；Nanyang Technological University（南洋理工大学）；Sun Yat-sen University（中山大学）；University of Chinese Academy of Sciences（中国科学院大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Peng Cheng Laboratory（鹏城实验室）
Clore: Interactive Pathology Image Segmentation with Click-based Local Refinement
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）；School of Artificial Intelligence and Data Science, University of Science and Technology of China（中国科学技术大学人工智能与数据科学学院）
D$^3$R-DETR: DETR with Dual-Domain Density Refinement for Tiny Object Detection in Aerial Images
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院航空信息研究所）；Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Chinese Academy of Sciences（中国科学院地理空间信息处理与应用系统重点实验室）；Key Laboratory of Target Cognition and Application Technology, Chinese Academy of Sciences（中国科学院目标认知与应用技术重点实验室）；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）
PRVQL: Progressive Knowledge-guided Refinement for Robust Egocentric Visual Query Localization
University of North Texas（北卡罗来纳州立大学）；University of Texas at Dallas（德克萨斯大学达拉斯分校）；U.S. Naval Research Laboratory（美国海军研究实验室）；Brookhaven National Laboratory（布鲁克海文国家实验室）
CycleSegNet: Object Co-segmentation with Cycle Refinement and Region Correspondence
Nanyang Technological University（南洋理工大学）；South China University of Technology（华南理工大学）；Pazhou Lab（琶洲实验室）；China University of Mining and Technology（中国矿业大学）
Deformable Feature Alignment and Refinement for Moving Infrared Dim-small Target Detection
School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）；School of Control Science and Engineering, Shandong University（山东大学控制科学与工程学院）
ArSDM: Colonoscopy Images Synthesis with Adaptive Refinement Semantic Diffusion Models
Shenzhen Research Institute of Big Data（深圳大数据研究院）；Shenzhen Health Development Research and Data Management Center（深圳市卫生健康发展研究和数据管理中心）；The Chinese University of Hong Kong（香港中文大学）；Sun Yat-sen University（中山大学）
Safe Self-Refinement for Transformer-based Domain Adaptation
Stony Brook University（石溪大学）；XPeng Motors（小鹏汽车）
无监督的领域自适应（UDA）旨在利用标签丰富的源域来解决相关未标记目标域上的任务。这是一个具有挑战性的问题，特别是当一个大的域之间的差距位于源和目标域。本文提出了一种新的解决方案SSRT（Safe Self-Refinement for Transformer-based领域自适应），从两个方面进行了改进。首先，由于视觉Transformer在各种视觉任务中的成功，我们为SSRT配备了一个Transformer骨干。我们发现，视觉Transformer与简单的对抗性适应的组合超过了最好的报告卷积神经网络（CNN）的结果，基于具有挑战性的DomainNet基准，显示其强大的可转移特征表示。其次，为了降低模型崩溃的风险，提高知识转移的有效性与大差距的领域之间，我们提出了一个安全的自我完善策略。具体而言，SSRT利用扰动的目标域数据的预测来细化模型。由于视觉Transformer的模型容量很大，并且在这种具有挑战性的任务中的预测可能是有噪声的，因此设计了一种安全的训练机制来自适应地调整学习配置。在几个广泛测试的UDA基准上进行了广泛的评估，SSRT始终获得最佳性能，包括在Bull-Home上的85.43%，在VisDA-2017上的88.76%和在DomainNet上的45.2%。
GReFEM: Multimodal LLMs as Zero-Shot Semantic Assistants for Physics-Guided 3D Mesh Refinement
Institute of Material Systems Modeling（材料系统建模研究所）；Helmholtz Zentrum Hereon（海德堡研究中心Hereon）；Institute of Material and Process Design（材料与加工设计研究所）；German Research Center for Artificial Intelligence（德国人工智能研究中心）
