Learning Fine-Grained Segmentation of 3D Shapes without Part Labels
Southwest University（西南大学）；State Key Laboratory of Virtual Reality Technology and Systems, Beihang University（北京航空航天大学虚拟现实技术与系统国家重点实验室）；National University of Defense Technology（国防科技大学）
Xiaogang Wang, Xun Sun, Xinyu Cao, Kai Xu, Bin Zhou
Learning-based 3D shape segmentation is usually formulated as a semantic labeling problem, assuming that all parts of training shapes are annotated with a given set of tags. This assumption, however, is impractical for learning fine-grained segmentation. Although most off-the-shelf CAD models are, by construction, composed of fine-grained parts, they usually miss semantic tags and labeling those fine-grained parts is extremely tedious. We approach the problem with deep clustering, where the key idea is to learn part priors from a shape dataset with fine-grained segmentation but no part labels. Given point sampled 3D shapes, we model the clustering priors of points with a similarity matrix and achieve part segmentation through minimizing a novel low rank loss. To handle highly densely sampled point sets, we adopt a divide-and-conquer strategy. We partition the large point set into a number of blocks. Each block is segmented using a deep-clustering-based part prior network trained in a category-agnostic manner. We then train a graph convolution network to merge the segments of all blocks to form the final segmentation result. Our method is evaluated with a challenging benchmark of fine-grained segmentation, showing state-of-the-art performance.
FGAseg: Fine-Grained Pixel-Text Alignment for Open-Vocabulary Semantic Segmentation
University of Science and Technology of China（中国科学技术大学）；Institute of Artificial Intelligence (TeleAI), China Telecom（中国电信人工智能研究院（TeleAI））；School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University（西北工业大学人工智能、光学与电子学院（iOPEN））
Med-DisSeg: Dispersion-Driven Representation Learning for Fine-Grained 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）；Guangdong Key Laboratory of Big Data Analysis and Processing（广东省大数据分析与处理重点实验室）
CoMLP: Cooperatively-Gated MLPs for Fine-Grained Cross-Modal Information Fusion in Medical Image Segmentation
Institute of Translational Medicine, Shanghai Jiao Tong University（上海交通大学转化医学研究院）；Zhongguancun Academy（中关村学院）；Zhongguancun Institute of Artificial Intelligence（中关村人工智能研究院）；School of Computer Science, the University of Sydney（悉尼大学计算机学院）；China United Network Communications Group Co., Ltd.（中国联合网络通信集团有限公司）
FLAIRBrainSeg: Fine-grained brain segmentation using FLAIR MRI only
Univ. Bordeaux（波尔多大学）；Bordeaux INP（波尔多国立综合理工学院）；CNRS（法国国家科学研究中心）；Inserm（法国国家健康与医学研究院）；CHU de Bordeaux（波尔多大学医院中心）；Universitat Politècnica de València（瓦伦西亚理工大学）；Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas（高级信息技术与通信应用研究所）
SISP: A Benchmark Dataset for Fine-grained Ship Instance Segmentation in Panchromatic Satellite Images
State Key Laboratory of Space-Ground Integrated Information Technology, CAST（天地一体化信息技术国家重点实验室）；College of Computer Science and Technology, Harbin Engineering University（哈尔滨工程大学计算机科学与技术学院）；College of Electrical and Mechanical Engineering, Dalian Minzu University（大连民族大学机电工程学院）；School of Geography and Ocean Science, Nanjing University（南京大学地理与海洋科学学院）
Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Tencent AI Lab（腾讯人工智能实验室）；The Chinese University of Hong Kong（香港中文大学）
FUSU: A Multi-temporal-source Land Use Change Segmentation Dataset for Fine-grained Urban Semantic Understanding
The University of Hong Kong（香港大学）；Sun Yat-Sen University（中山大学）；National Supercomputing Center（国家超级计算中心）；Tsinghua University（清华大学）；Peng Cheng Labratory（鹏城实验室）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）
CLIP-TNseg: A Multi-Modal Hybrid Framework for Thyroid Nodule Segmentation in Ultrasound Images
School of Electronic and Information, Beihang University（北京航空航天大学电子信息学院）；Peking University First Hospital（北京大学第一医院）；Unmanned System Research Institute, Beihang University（北京航空航天大学无人系统研究院）
Technical Report for ICRA 2026 GOOSE 2D Fine-Grained Semantic Segmentation Challenge: Exploring Query-Based Segmentation and Increased Spatial Context for Outdoor Scene Understanding
Anatomy-Aware Text-Visual Fusion with Dual-Perspective Prompts for Fine-Grained Lumbar Spine Segmentation
College of Computer and Data Science（计算机与数据科学学院）；Shandong Technology and Business University（山东科技商务大学）；Engineering Research Center of Big Data Intelligence (Fuzhou University)（大数据智能工程研究中心（福州大学））；The Chinese University of Hong Kong（香港中文大学）；Case Western Reserve University（凯斯西储大学）
Uncertainty Estimation for Safety-critical Scene Segmentation via Fine-grained Reward Maximization
The Chinese University of Hong Kong（香港中文大学）；University Hospital of Augsburg（奥格斯堡大学医院）；Harvard Medical School & Massachusetts General Hospital（哈佛医学院与麻省总医院）
TB-Net: A Three-Stream Boundary-Aware Network for Fine-Grained Pavement Disease Segmentation
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Towards Fine-grained Renal Vasculature Segmentation: Full-Scale Hierarchical Learning with FH-Seg
Data Science Institute, Vanderbilt University（范德堡大学数据科学研究所）；Big Data Institute, Central South University（中南大学大数据研究所）；Vanderbilt University Medical Center（范德堡大学医学中心）
Multi-grained Temporal Prototype Learning for Few-shot Video Object Segmentation
Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Northwestern Polytechnical University（西北工业大学）；National University of Singapore（新加坡国立大学）；Australian National University（澳大利亚国立大学）；Linköping University（林雪平大学）
Technical Report for the ICRA 2026 GOOSE 2D Fine-Grained Semantic Segmentation Challenge: Pretraining-Diverse Ensemble of Foundation Vision Encoders for Robust Outdoor Scene Understanding
LION Lab, Hefei University of Technology（合肥工业大学 LION 实验室）；University of Macau（澳门大学）
Fine-grained Metrics for Point Cloud Semantic Segmentation
School of Automation, Nanjing University of Science and Technology（南京理工大学自动化学院）；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）
Adapting SAM to Nuclei Instance Segmentation and Classification via Cooperative Fine-Grained Refinement
organization= College of Computer；Data Science , addressline= Fuzhou University , city= Fuzhou , country= China；organization= School of Mechanical Engineering；Automation , addressline= Fuzhou University , city= Fuzhou , country= China；organization= College of Physics；Information Engineering , addressline= Fuzhou University , city= Fuzhou , country= China
