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Revealing the Power of Spatial-Temporal Masked Autoencoders in Multivariate Time Series Forecasting
To address these issues, we propose Spatial-Temporal Masked Autoencoders (STMAE), an MTS forecasting framework that leverages masked autoencoders to enhance the performance of spatial-temporal baseline models.
Towards Consistent Stochastic Human Motion Prediction via Motion Diffusion
Stochastic Human Motion Prediction (HMP) aims to predict multiple possible upcoming pose sequences based on past human motion trajectories.
Towards Accurate Human Motion Prediction via Iterative Refinement
To address the problem, in this work we propose FreqMRN, a human motion prediction framework that takes into account both the kinematic structure of the human body and the temporal smoothness nature of motion.
Ranked #2 on
Human Pose Forecasting
on 3DPW
Dynamic Graph Node Classification via Time Augmentation
Node classification on dynamic graphs is challenging for two reasons.
Globally Variance-Constrained Sparse Representation and Its Application in Image Set Coding
Therefore, introducing an accurate rate-constraint in sparse coding and dictionary learning becomes meaningful, which has not been fully exploited in the context of sparse representation.
