TimeSformer
TimeSformer
**TimeSformer** is a [convolution](https://paperswithcode.com/method/convolution)-free approach to video classification built exclusively on self-attention over space and time. It adapts the standard [Transformer](https://paperswithcode.com/method/transformer) architecture to video by enabling spatiotemporal feature learning directly from a sequence of frame-level patches. Specifically, the method adapts the image model [[Vision Transformer](https://paperswithcode.com/method/vision-transformer)](https//www.paperswithcode.com/method/vision-transformer) (ViT) to video by extending the self-attention mechanism from the image space to the space-time 3D volume. As in ViT, each patch is linearly mapped into an embedding and augmented with positional information. This makes it possible to interpret the resulting sequence of vector
Is Space-Time Attention All You Need for Video Understanding?
https://paperswithcode.com/paper/is-space-time-attention-all-you-need-for
Is Space-Time Attention All You Need for Video Understanding?