Estimation Task
Estimation tasks, broadly defined as the process of inferring unknown parameters or values from available data, are central to numerous scientific and engineering disciplines. Current research emphasizes developing robust and efficient estimation methods across diverse data types and model complexities, focusing on techniques like Bayesian frameworks, deep neural networks (including graph convolutional networks), and simulation-based inference. These advancements are driving improvements in areas ranging from medical diagnosis and robotics to power systems optimization and material science, enabling more accurate predictions and informed decision-making.
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Papers - Page 4
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Mobile Augmented Reality Framework with Fusional Localization and Pose Estimation
Songlin Hou, Fangzhou Lin, Yunmei Huang, Zhe Peng, Bin XiaoSurgRIPE challenge: Benchmark of Surgical Robot Instrument Pose Estimation
Haozheng Xu, Alistair Weld, Chi Xu, Alfie Roddan, Joao Cartucho, Mert Asim Karaoglu, Alexander Ladikos, Yangke Li, Yiping Li, Daiyun Shen+10Spiking monocular event based 6D pose estimation for space application
Jonathan Courtois, Benoît Miramond, Alain PegatoquetUniversal Features Guided Zero-Shot Category-Level Object Pose Estimation
Wentian Qu, Chenyu Meng, Heng Li, Jian Cheng, Cuixia Ma, Hongan Wang, Xiao Zhou, Xiaoming Deng, Ping TanUnsupervised Domain Adaptation for Occlusion Resilient Human Pose Estimation
Arindam Dutta, Sarosij Bose, Saketh Bachu, Calvin-Khang Ta, Konstantinos Karydis, Amit K. Roy-Chowdhury