On public benchmarks, current action recognition techniques have achieved great success. However, when used in real-world applications, e.g. sport analysis, which requires the capability of parsing an activity into phases and differentiating between subtly different actions, their performances remain far from being satisfactory. To take action recognition to a new level, we develop FineGym, a new dataset built on top of gymnastic videos. Compared to existing action recognition datasets, FineGym is distinguished in richness, quality, and diversity. In particular, it provides temporal annotations at both action and sub-action levels with a three-level semantic hierarchy. For example, a "balance beam" event will be annotated as a sequence of elementary sub-actions derived from five sets: "leap-jump-hop", "beam-turns", "flight-salto", "flight-handspring", and "dismount", where the sub-action in each set will be further annotated with finely defined class labels. This new level of granularity presents significant challenges for action recognition, e.g. how to parse the temporal structures from a coherent action, and how to distinguish between subtly different action classes. We systematically investigate representative methods on this dataset and obtain a number of interesting findings. We hope this dataset could advance research towards action understanding.
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）
Wavelet-Decoupling Contrastive Enhancement Network for Fine-Grained Skeleton-Based Action Recognition
Beijing Engineering Research Center of Mixed Reality and Advanced Display（北京混合现实与先进显示工程研究中心）；School of Optics and Photonics, Beijing Institute of Technology（北京理工大学光电学院）
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）
FinePhys: Fine-grained Human Action Generation by Explicitly Incorporating Physical Laws for Effective Skeletal Guidance
Unmanned System Research Institute, Northwestern Polytechnical University, Xi’an, China（无人系统研究院、西北工业大学、西安，中国）；School of Software, Northwestern Polytechnical University, Xi’an, China（软件学院、西北工业大学、西安，中国）；School of Automation, Northwestern Polytechnical University, Xi’an, China（自动化学院、西北工业大学、西安，中国）；School of Astronautics, Northwestern Polytechnical University, Xi’an, China（航天学院、西北工业大学、西安，中国）
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）
SeFAR: Semi-supervised Fine-grained Action Recognition with Temporal Perturbation and Learning Stabilization
Unmanned System Research Institute, Northwestern Polytechnical University（西北工业大学无人系统研究院）；School of Automation, Northwestern Polytechnical University（西北工业大学自动化学院）；School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机学院）；School of Software, Northwestern Polytechnical University（西北工业大学软件学院）
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）
