# Video Self-supervised Learning

VideoMAEv2-giant is an ultra-large-scale video classification model based on self-supervised learning, employing a dual masking strategy for pretraining.

VideoMAEv2-Large is a large-scale video feature extraction model pre-trained with self-supervision on the UnlabeldHybrid-1M dataset

VideoMAE is a video pretraining model based on Masked Autoencoder (MAE), fine-tuned on the Kinetics-400 dataset through self-supervised learning, suitable for video classification tasks.

VideoMAE is a video self-supervised pretraining model based on Masked Autoencoder (MAE), which learns internal video representations by predicting pixel values of masked video patches.

VideoMAE is a video self-supervised pre-training model based on Masked Autoencoder (MAE), which learns video representations by predicting pixel values of masked video patches

VideoMAE is a video self-supervised pre-training model based on Masked Autoencoder (MAE), fine-tuned on the Kinetics-400 dataset for video classification tasks.

VideoMAE is a video self-supervised pre-training model based on Masked Autoencoder (MAE), fine-tuned on the Kinetics-400 dataset for video classification tasks.

VideoMAE is a video self-supervised pretraining model based on Masked Autoencoder (MAE), which learns internal video representations through masked patch prediction, suitable for downstream tasks like video classification.