# Video Classification

VideoMAE is a masked autoencoder model for video, pretrained with self-supervision and fine-tuned on the Kinetics-400 dataset, suitable for video classification tasks.

ViViT is an extension of the Vision Transformer (ViT) for video processing, particularly suitable for video classification tasks.

ViViT is an extension of the Vision Transformer (ViT) for video processing, primarily used for downstream tasks such as video classification.

TimeSformer is a video understanding model based on spatio-temporal attention mechanisms, pre-trained and fine-tuned on the Kinetics-400 dataset.

X-CLIP is an extended version of CLIP for general video-language understanding, supporting zero-shot, few-shot, or fully supervised video classification tasks.

X-CLIP is a minimalist extension of CLIP for general video-language understanding, trained contrastively on (video, text) pairs, suitable for zero-shot, few-shot, or fully supervised video classification as well as video-text retrieval tasks.

X-CLIP is an extended version of CLIP for general video-language understanding, supporting video classification and video-text retrieval tasks.

X-CLIP is an extended version of CLIP for general video-language understanding, trained via contrastive learning on video-text pairs, supporting zero-shot, few-shot, and fully supervised video classification tasks.