VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
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Updated
May 25, 2024 - Python
VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
[Survey] Masked Modeling for Self-supervised Representation Learning on Vision and Beyond (https://arxiv.org/abs/2401.00897)
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Official Implementation of the CrossMAE paper: Rethinking Patch Dependence for Masked Autoencoders
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📗 This repository provides an in-depth exploration of the predictive linear regression model tailored for Jamboree Institute students' data, with the goal of assisting their admission to international colleges. The analysis encompasses the application of Ridge, Lasso, and ElasticNet regressions to enhance predictive accuracy and robustness.
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Compute the mean absolute error (MAE) incrementally.
Compute a moving mean absolute error (MAE) incrementally.
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A collection of literature after or concurrent with Masked Autoencoder (MAE) (Kaiming He el al.).
[SHREC24] Skeleton-based Self-Supervised Learning For Dynamic Hand Gesture Recognition
Skeleton-based Self-Supervised Feature Extraction for Improved Dynamic Hand Gesture Recognition
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Artificial intelligence (AI, ML, DL) performance metrics implemented in Python
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