  In this paper, we propose a novel token selective attention approach, ToSA,
which can identify tokens that need to be attended as well as those that can
skip a transformer layer. More specifically, a token selector parses the
current attention maps and predicts the attention maps for the next layer,
which are then used to select the important tokens that should participate in
the attention operation. The remaining tokens simply bypass the next layer and
are concatenated with the attended ones to re-form a complete set of tokens. In
this way, we reduce the quadratic computation and memory costs as fewer tokens
participate in self-attention while maintaining the features for all the image
patches throughout the network, which allows it to be used for dense prediction
tasks. Our experiments show that by applying ToSA, we can significantly reduce
computation costs while maintaining accuracy on the ImageNet classification
benchmark. Furthermore, we evaluate on the dense prediction task of monocular
depth estimation on NYU Depth V2, and show that we can achieve similar depth
prediction accuracy using a considerably lighter backbone with ToSA.
