Cross-Camera Cow Identification via Disentangled Representation Learning
College of Information Science and Engineering, Shandong Agricultural University（信息科学与工程学院，山东农业大学）；Centre for Vision Speech and Signal Processing (CVSSP), University of Surrey（视觉语音与信号处理中心（CVSSP）， Surrey 大学）；Australian Institute for Machine Learning, University of Adelaide（机器学习研究院，阿德莱德大学）
Precise identification of individual cows is a fundamental prerequisite for comprehensive digital management in smart livestock farming. While existing animal identification methods excel in controlled, single-camera settings, they face severe challenges regarding cross-camera generalization. When models trained on source cameras are deployed to new monitoring nodes characterized by divergent illumination, backgrounds, viewpoints, and heterogeneous imaging properties, recognition performance often degrades dramatically. This limits the large-scale application of non-contact technologies in dynamic, real-world farming environments. To address this challenge, this study proposes a cross-camera cow identification framework based on disentangled representation learning. This framework leverages the Subspace Identifiability Guarantee (SIG) theory in the context of bovine visual recognition. By modeling the underlying physical data generation process, we designed a principle-driven feature disentanglement module that decomposes observed images into multiple orthogonal latent subspaces. This mechanism effectively isolates stable, identity-related biometric features that remain invariant across cameras, thereby substantially improving generalization to unseen cameras. We constructed a high-quality dataset spanning five distinct camera nodes, covering heterogeneous acquisition devices and complex variations in lighting and angles. Extensive experiments across seven cross-camera tasks demonstrate that the proposed method achieves an average accuracy of 86.0%, significantly outperforming the Source-only Baseline (51.9%) and the strongest cross-camera baseline method (79.8%). This work establishes a subspace-theoretic feature disentanglement framework for collaborative cross-camera cow identification, offering a new paradigm for precise animal monitoring in uncontrolled smart farming environments.
Universal Bovine Identification via Depth Data and Deep Metric Learning
Bristol Veterinary School, University of Bristol（布里斯托尔大学布里斯托尔兽医学院）；University of Bristol（布里斯托尔大学）；Centre for Machine Vision, University of the West of England（西英格兰大学机器视觉中心）；University of the West of England（西英格兰大学）；School of Computer Science, University of Bristol（布里斯托尔大学计算机科学学院）
Disentangled Generation and Aggregation for Robust Radiance Fields
School of Electronic and Computer Engineering, Peking University（北京大学电子与计算机工程学院）；Peng Cheng Laboratory（鹏城实验室）；School of Computer Science, University of Birmingham（伯明翰大学计算机学院）
STD-GS: Exploring Frame-Event Interaction for SpatioTemporal-Disentangled Gaussian Splatting to Reconstruct High-Dynamic Scene
National Key Lab of Multispectral Information Intelligent Processing Technology（多谱信息智能处理国家级重点实验室）；School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（人工智能与自动化学院，华中科技大学）；School of Computing, National University of Singapore（计算机学院，新加坡国立大学）
Easi3R: Estimating Disentangled Motion from DUSt3R Without Training
Zhejiang University（浙江大学）；Westlake University（西湖大学）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；University of Tübingen, Tübingen AI Center（图宾根大学，图宾根AI中心）
Hierarchical Context Alignment with Disentangled Geometric and Temporal Modeling for Semantic Occupancy Prediction
Shanghai Jiao Tong University（上海交通大学）；Eastern Institute of Technology（东部技术研究所）；Ningbo Institute of Digital Twin（宁波数字孪生研究所）；University of Adelaide（阿德莱德大学）；Tokyo Institute of Technology（东京技术大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Text-guided Feature Disentanglement for Cross-modal Gait Recognition
Fujian Key Laboratory of Urban Intelligent Sensing and Computing, Xiamen University（福建城市智能感知与计算重点实验室，厦门大学）；Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University（多媒体可信感知与高效计算重点实验室，中华人民共和国教育部，厦门大学）
Disentangling representations of retinal images with generative models
Hertie Institute for AI in Brain Health, Faculty of Medicine, University of Tübingen（人工智能与脑健康赫特研究所，医学院，图宾根大学）；Tübingen AI Center, Tübingen, Germany（图宾根人工智能中心，图宾根，德国）；Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism UDEM, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland（糖尿病、内分泌学、营养医学和代谢学部门UDEM，伯尔尼大学医院，伯尔尼大学，伯尔尼，瑞士）；Department of Computer Science, University of Tübingen, Tübingen, Germany（计算机科学系，图宾根大学，图宾根，德国）
Cross-Dataset Person Re-Identification via Unsupervised Pose Disentanglement and Adaptation
National Taiwan University（台湾大学）；MOST Joint Research Center for AI Technology and All Vista Healthcare（科技部AI技术与全视医疗照护联合研究中心）；ASUS Intelligent Cloud Services（华硕智能云服务）
Dual-Stream Reciprocal Disentanglement Learning for Domain Adaptation Person Re-Identification
Kunming University of Science and Technology（昆明理工大学）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））；Shenzhen Research Institute of Big Data（深圳大数据研究院）
