VideoMAE (base sized model, fine tuned on Kinetics 400) VideoMAE model pre trained for 1600 epochs in a self supervised way and fine tuned in a supervised way on Kinetics 400. It was introduced in the paper VideoMAE: Masked Autoencoders are Data Efficient Learners for Self Supervised Video Pre Training by Tong et al. and first released in this repository. Disclaimer: The team releasing VideoMAE did not write a model card for this model so this model card has been written by the Hugging Face team. Model description VideoMAE is an extension of Masked Autoencoders (MAE) to video. The architecture of the model is very similar to that of a standard Vision Transformer (ViT), with a decoder on top for predicting pixel values for masked patches. Videos are presented to the model as a sequence of fixed size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds fixed sinus/cosinus position embeddings before feeding the sequence to the layers of the Transformer encoder. By pre training the model, it learns an inner representation of videos that can then be used to extract features u…
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