CoaT
Co-Scale Conv-attentional Image Transformer
**Co-Scale Conv-Attentional Image Transformer** (CoaT) is a [Transformer](https://paperswithcode.com/method/transformer)-based image classifier equipped with co-scale and conv-attentional mechanisms. First, the co-scale mechanism maintains the integrity of Transformers' encoder branches at individual scales, while allowing representations learned at different scales to effectively communicate with each other. Second, the conv-attentional mechanism is designed by realizing a relative position embedding formulation in the factorized attention module with an efficient [convolution](https://paperswithcode.com/method/convolution)-like implementation. CoaT empowers image Transformers with enriched multi-scale and contextual modeling capabilities.
Co-Scale Conv-Attentional Image Transformers
https://paperswithcode.com/paper/co-scale-conv-attentional-image-transformers
Co-Scale Conv-Attentional Image Transformers