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Researchers from CMU and Max Planck Institute Unveil WHAM: A Groundbreaking AI Approach for Precise and Efficient 3D Human Motion Estimation from Video

3D human motion reconstruction is a complex process that involves accurately capturing and modeling the movements of a human subject in three dimensions. This job becomes even more challenging when dealing with videos captured by a moving camera in real-world settings, as they often contain issues like foot sliding. However, a team of researchers from…

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Comparing Outlier Detection Methods | by John Andrews | Dec, 2023

Using batting stats from Major League Baseball’s 2023 season Shohei Ohtani, photo by Erik Drost on Flikr, CC BY 2.0Outlier detection is an unsupervised machine learning task to identify anomalies (unusual observations) within a given data set. This task is helpful in many real-world cases where our available dataset is already “contaminated” by anomalies. Scikit-learn…

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This AI Paper Introduces RTMO: A Breakthrough in Real-Time Multi-Person Pose Estimation Using Dual 1-D Heatmaps

The field of pose estimation, which involves determining the position and orientation of an object in space, is a rapidly evolving area, with researchers continuously developing new methods to improve its accuracy and performance. Researchers from three highly regarded institutions – Tsinghua Shenzhen International Graduate School, Shanghai AI Laboratory, and Nanyang Technological University – have…

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This AI Paper Introduces EdgeSAM: Advancing Machine Learning for High-Speed, Efficient Image Segmentation on Edge Devices

The Segment Anything Model (SAM) is an AI-powered model that segments images for object detection and recognition. It is an effective solution for various computer vision tasks. However, SAM is not optimized for edge devices, which can lead to retarded performance and high resource consumption. Researchers from S-Lab Nanyang Technological University and Shanghai Artificial Intelligence…

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