Fine-grained 3D shape classification is important for shape understanding and analysis, which poses a challenging research problem. However, the studies on the fine-grained 3D shape classification have rarely been explored, due to the lack of fine-grained 3D shape benchmarks. To address this issue, we first introduce a new 3D shape dataset (named FG3D dataset) with fine-grained class labels, which consists of three categories including airplane, car and chair. Each category consists of several subcategories at a fine-grained level. According to our experiments under this fine-grained dataset, we find that state-of-the-art methods are significantly limited by the small variance among subcategories in the same category. To resolve this problem, we further propose a novel fine-grained 3D shape classification method named FG3D-Net to capture the fine-grained local details of 3D shapes from multiple rendered views. Specifically, we first train a Region Proposal Network (RPN) to detect the generally semantic parts inside multiple views under the benchmark of generally semantic part detection. Then, we design a hierarchical part-view attention aggregation module to learn a global shape representation by aggregating generally semantic part features, which preserves the local details of 3D shapes. The part-view attention module hierarchically leverages part-level and view-level attention to increase the discriminability of our features. The part-level attention highlights the important parts in each view while the view-level attention highlights the discriminative views among all the views of the same object. In addition, we integrate a Recurrent Neural Network (RNN) to capture the spatial relationships among sequential views from different viewpoints. Our results under the fine-grained 3D shape dataset show that our method outperforms other state-of-the-art methods.
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）
Scan2Part: Fine-grained and Hierarchical Part-level Understanding of Real-World 3D Scans
Skolkovo Institute of Science and Technology（斯科尔科沃科学技术学院）
我们提出了Scan 2 Part，一种在现实世界中分割物体各个部分的方法，嘈杂的室内RGB-D扫描。为此，我们在室内场景中的对象的不同的部分层次结构，并探讨其对场景理解模型的影响。具体来说，我们使用一个稀疏的U-Net为基础的架构，通过利用多尺度特征层次结构捕获底层3D扫描几何结构的精细尺度细节。为了训练我们的方法，我们介绍了Scan 2 Part数据集，这是第一个大规模的集合，提供详细的语义标签在现实世界中的设置。在2 cm $^3$和5 cm $^3$两种空间分辨率下，我们总共提供了2，477个ShapeNet对象和1，506个ScanNet场景的53，618个PartNet部分之间的242，081个对应关系。作为输出，我们能够预测细粒度的每个对象部分标签，即使几何体粗糙或部分缺失。
Fine-Grained Representation Learning and Recognition by Exploiting Hierarchical Semantic Embedding
Sun Yat-sen University（中山大学）；South China Agricultural University（华南农业大学）；University of Electronic Science and Technology of China（电子科技大学）；Guilin University of Electronic Technology（桂林电子科技大学）
HMGIE: Hierarchical and Multi-Grained Inconsistency Evaluation for Vision-Language Data Cleansing
School of Data Science, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）数据科学学院）；University at Buffalo, State University of New York（纽约州立大学布法罗分校）
Fine-grained Hierarchical Crop Type Classification from Integrated Hyperspectral EnMAP Data and Multispectral Sentinel-2 Time Series: A Large-scale Dataset and Dual-stream Transformer Method
Jockey Club STEM Lab of Quantitative Remote Sensing, Department of Geography, The University of Hong Kong, Hong Kong, China（香港大学地理系定量遥感实验室）；Department of Aerospace Intelligent Science and Technology, School of Astronautics, Beihang University, Beijing, China（北航航天智能科学与技术系）
Anchor-based Multi-view Subspace Clustering with Hierarchical Feature Descent
College of Computer, National University of Defense Technology（国防科技大学计算机学院）；Intelligent Game and Decision Lab（智能游戏与决策实验室）；College of Intelligence Science and Technology, National University of Defense Technology（国防科技大学智能科学学院）
Hierarchical Classification for Improved Histopathology Image Analysis
School of Electrical Engineering, Korea University, Seoul, Korea（韩国大学电气工程学院）；Department of Hospital Pathology, The Catholic University of Korea College of Medicine, Seoul, Korea（韩国天主大学医学院医院病理系）
Balanced Hierarchical Contrastive Learning with Decoupled Queries for Fine-grained Object Detection in Remote Sensing Images
School of Computer Science and Engineering, Nanjing University of Science and Technology, China（南京理工大学计算机科学与工程学院）；College of Computer Science, Zhejiang University, China（浙江大学计算机学院）
FlexiGrad: Adaptive Gradient Modulation for Hierarchical Fine-Grained Classification
Beijing University of Posts and Telecommunications（北京邮电大学）；Beijing Key Laboratory of Multimodal Data Intelligent Perception and Governance（北京多模态数据智能感知与治理重点实验室）
HMIL: Hierarchical Multi-Instance Learning for Fine-Grained Whole Slide Image Classification
The Hong Kong University of Science and Technology（香港科技大学）；Harvard University（哈佛大学）；The Chinese University of Hong Kong（香港中文大学）；Peking University Shenzhen Hospital（北京大学深圳医院）
Cross-Hierarchical Bidirectional Consistency Learning for Fine-Grained Visual Classification
University of Science and Technology of China（中国科学技术大学）；Key Laboratory of the Ministry of Education for Mathematical Foundations and Applications of Digital Technology（教育部数学基础与数字技术应用重点实验室）
School of Computer Science, Wuhan University（武汉大学计算机学院）；School of Artificial Intelligence, Wuhan University（武汉大学人工智能学院）；School of Electronic Information, Wuhan University（武汉大学电子信息学院）
Fine-grained text-driven dual-human motion generation via dynamic hierarchical interaction
State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China（虚拟现实技术与系统国家重点实验室，北京航空航天大学，北京，中国）；Department of Computer Science, University of Durham, UK（计算机科学系，达勒姆大学，英国）；Zhongguancun Laboratory, Beijing, China（中关村实验室，北京，中国）
HiVG: Hierarchical Multimodal Fine-grained Modulation for Visual Grounding
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Pengcheng Laboratory（鹏城实验室）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））
A Unified Hierarchical Framework for Fine-grained Cross-view Geo-localization over Large-scale Scenarios
School of Electronics and Communication Engineering, the Shenzhen Campus of Sun Yat-sen University（中山大学电子与通信工程学院（深圳校区））；Sun Yat-sen University（中山大学）；College of Electronic Science and Technology, Aviation University of Air Force（空军工程大学电子科学与技术学院）
H3Former: Hypergraph-based Semantic-Aware Aggregation via Hyperbolic Hierarchical Contrastive Loss for Fine-Grained Visual Classification
College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）；College of Software, Jilin University（吉林大学软件学院）；School of Software, Tsinghua University（清华大学软件学院）
Beyond Binary Preference: Aligning Diffusion Models to Fine-grained Criteria by Decoupling Attributes
Zhejiang University（浙江大学）；University of Electronic Science and Technology of China（电子科技大学）；Peking University（北京大学）；University of Nottingham Ningbo China（诺丁汉大学宁波分校）
AuricularWorld: Hierarchical Action-Guided World Modeling for Fine-Grained Auricular Structure Segmentation from CT Scans
Plastic Surgery Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College（中国医学科学院北京协和医学院整形外科医院）；The Chinese University of Hong Kong（香港中文大学）；The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））
Interpretable Single-View 3D Gaussian Splatting using Unsupervised Hierarchical Disentangled Representation Learning
Shanghai Jiao Tong University（上海交通大学）；Ningbo Institute of Digital Twin, Eastern Institute of Technology（东方理工大学宁波数字孪生研究院）；Hong Kong Polytechnic University（香港理工大学）
Fine-Grained VLM Fine-tuning via Latent Hierarchical Adapter Learning
Information Materials and Intelligent Sensing Laboratory of Anhui Province（安徽省信息材料与智能感知实验室）；Anhui Provincial Key Laboratory of Multimodal Cognitive Computation（安徽省多模态认知计算重点实验室）；School of Computer Science and Technology, Anhui University（安徽大学计算机科学与技术学院）
HieroAction: Hierarchically Guided VLM for Fine-Grained Action Analysis
Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, School of Artificial Intelligence, Beihang University（北京航空航天大学人工智能学院未来区块链与隐私计算高精尖创新中心）；Psyche AI Inc（Psyche AI 公司）；National University of Singapore（新加坡国立大学）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；MemTensor (Shanghai) Technology Co., Ltd.（MemTensor（上海）科技有限公司）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Jinan University（暨南大学）；Nanjing University of Science and Technology（南京理工大学）；Hefei University of Technology（合肥工业大学）
