  In this paper, we present Co-scale conv-attentional image Transformers
(CoaT), a 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; we design a series of serial and parallel blocks to realize
the co-scale mechanism. Second, we devise a conv-attentional mechanism by
realizing a relative position embedding formulation in the factorized attention
module with an efficient convolution-like implementation. CoaT empowers image
Transformers with enriched multi-scale and contextual modeling capabilities. On
ImageNet, relatively small CoaT models attain superior classification results
compared with similar-sized convolutional neural networks and image/vision
Transformers. The effectiveness of CoaT's backbone is also illustrated on
object detection and instance segmentation, demonstrating its applicability to
downstream computer vision tasks.
