Local-HDP (for Local Hierarchical Dirichlet Process) is a hierarchical Bayesian method that has recently been used for open-ended 3D object category recognition. This method has been proven to be efficient in real-time robotic applications. However, the method is not robust to a high degree of occlusion. We address this limitation in two steps. First, we propose a novel semantic 3D object-parts segmentation method that has the flexibility of Local-HDP. This method is shown to be suitable for open-ended scenarios where the number of 3D objects or object parts is not fixed and can grow over time. We show that the proposed method has a higher percentage of mean intersection over union, using a smaller number of learning instances. Second, we integrate this technique with a recently introduced argumentation-based online incremental learning method, thereby enabling the model to handle a high degree of occlusion. We show that the resulting model produces an explicit set of explanations for the 3D object category recognition task.
DeOccNet: Learning to See Through Foreground Occlusions in Light Fields
College of Electronic Science and Technology, National University of Defense Technology（国防科技大学电子科学与技术学院）；School of Electronics and Communication Engineering, Sun Yat-sen University（中山大学电子与通信工程学院）
Dynamic Facial Expression Recognition under Partial Occlusion with Optical Flow Reconstruction
Centre de Recherche en Informatique Signal et Automatique de Lille（里尔计算机科学、信号与自动化研究中心）；Univ. Lille（里尔大学）；CNRS（法国国家科学研究中心）；Centrale Lille（里尔中央理工学院）；Junia（Junia工程学院）；University of Western Australia（西澳大利亚大学）
OccludeNet: A Causal Journey into Mixed-View Actor-Centric Video Action Recognition under Occlusions
Sanya Science and Education Innovation Park, Wuhan University of Technology, Sanya 572025, China（武汉理工大学三亚科技创新园）；Hubei Key Laboratory of Transportation Internet of Things, Wuhan University of Technology, Wuhan 430070, China（武汉理工大学交通物联网重点实验室）；State Key Laboratory for Multimedia Info Processing, Peking University, Beijing 100871, China（北京大学信息处理国家重点实验室）；Hubei Key Laboratory of Big Data Intelligent Analysis, Hubei University, Wuhan 430062, China（湖北大学大数据智能分析重点实验室）；National Engineering Research Center for Multimedia Software, Wuhan University, Wuhan 430072, China（武汉大学多媒体软件工程研究中心）；Department of Electrical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan（清华大学电子工程系）
针对视频动作识别中遮挡数据不足的问题，本文构建涵盖真实与合成场景的大规模 OccludeNet 数据集，并提出基于结构因果模型、后门调整和反事实推理的 CAR 方法以增强遮挡鲁棒性。
