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Showing posts from January 5, 2024

Finetuning VideoMAE

  VideoMAE is a self-supervised video pre-training method that uses masked autoencoders to learn data-efficient video representations. The method is based on video masking with a high ratio, which improves the performance of video reconstruction and the generalization of video representations on small datasets. The authors of the paper show that VideoMAE is a data-efficient learner for self-supervised video pre-training, and that it can achieve impressive results on very small datasets without using any extra data. The code for VideoMAE is available on GitHub . VideoMAE architecture What is a Masked Encoder A masked autoencoder is a type of neural network that can learn to extract and map meaningful latent representations into high-dimensional space from data by training on large datasets of input samples. The method is based on masking random patches of the input image and reconstructing the missing pixels. It is based on two core designs: an asymmetric encoder-decoder architecture,

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