XCiT
XCiT
**Cross-Covariance Image Transformers**, or **XCiT**, is a type of [vision transformer](https://paperswithcode.com/methods/category/vision-transformer) that aims to combine the accuracy of [conventional transformers](https://paperswithcode.com/methods/category/transformers) with the scalability of [convolutional architectures](https://paperswithcode.com/methods/category/convolutional-neural-networks). 

The [self-attention operation](https://paperswithcode.com/method/scaled) underlying transformers yields global interactions between all tokens, i.e. words or image patches, and enables flexible modelling of image data beyond the local interactions of convolutions. This flexibility, however, comes with a quadratic complexity in time and memory, hindering application to long sequences and high-resolution images. The authors propose a “transposed” version of self-attention called [cross-covariance attention](https://paperswithcode.com/method/cross-covariance-attention) that operates across feature channels rather than tokens, where the interactions are based on the cross-covariances matrix between keys and queries.
XCiT: Cross-Covariance Image Transformers
https://paperswithcode.com/paper/xcit-cross-covariance-image-transformers
XCiT: Cross-Covariance Image Transformers