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Title: SkeletonMAE: Spatial-Temporal Masked Autoencoders for Self-supervised Skeleton Action Recognition
Self-supervised skeleton-based action recognition has attracted more attention in recent years. By utilizing the unlabeled data, more generalizable features can be learned to alleviate the overfitting problem and reduce the demand for massive labeled training data. Inspired by the MAE [1], we propose a spatial-temporal masked autoencoder framework for self-supervised 3D skeleton-based action recognition (SkeletonMAE). Following MAE's masking and reconstruction pipeline, we utilize a skeleton-based encoder-decoder transformer architecture to reconstruct the masked skeleton sequences. A novel masking strategy, named Spatial-Temporal Masking, is introduced in terms of both joint-level and frame-level for the skeleton sequence. This pre-training strategy makes the encoder output generalizable skeleton features with spatial and temporal dependencies. Given the unmasked skeleton sequence, the encoder is fine-tuned for the action recognition task. Extensive ex- periments show that our SkeletonMAE achieves remarkable performance and outperforms the state-of-the-art methods on both NTU RGB+D 60 and NTU RGB+D 120 datasets.  more » « less
Award ID(s):
1840080
PAR ID:
10464795
Author(s) / Creator(s):
Date Published:
Journal Name:
Workshop in International Conference on Multimedia and Expo (ICME), 2023
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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