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Found 7 papers, 3 papers with code
Quater-GCN: Enhancing 3D Human Pose Estimation with Orientation and Semi-supervised Training
3D human pose estimation is a vital task in computer vision, involving the prediction of human joint positions from images or videos to reconstruct a skeleton of a human in three-dimensional space.
An Animation-based Augmentation Approach for Action Recognition from Discontinuous Video
Action recognition, an essential component of computer vision, plays a pivotal role in multiple applications.
Respiratory motion forecasting with online learning of recurrent neural networks for safety enhancement in externally guided radiotherapy
We use UORO, SnAp-1, and DNI to forecast each marker's 3D position with horizons (the time interval in advance for which the prediction is made) h<=2. 1s and compare them with RTRL, least mean squares, and linear regression.
GTAutoAct: An Automatic Datasets Generation Framework Based on Game Engine Redevelopment for Action Recognition
Current datasets for action recognition tasks face limitations stemming from traditional collection and generation methods, including the constrained range of action classes, absence of multi-viewpoint recordings, limited diversity, poor video quality, and labor-intensive manually collection.
Prediction of the motion of chest internal points using a recurrent neural network trained with real-time recurrent learning for latency compensation in lung cancer radiotherapy
The amplitude of the motion of the tracked points ranged from 12. 0mm to 22. 7mm.
Cross-Task Consistency Learning Framework for Multi-Task Learning
We theoretically prove that both losses help the model learn more efficiently and that cross-task consistency loss is better in terms of alignment with the straight-forward predictions.
Prediction of the Position of External Markers Using a Recurrent Neural Network Trained With Unbiased Online Recurrent Optimization for Safe Lung Cancer Radiotherapy
Prediction with online learning of recurrent neural networks (RNN) allows for adaptation to non-stationary respiratory signals, but classical methods such as RTRL and truncated BPTT are respectively slow and biased.
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Multivariate Time Series Forecasting
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