  Transformer has recently gained considerable popularity in low-level vision
tasks, including image super-resolution (SR). These networks utilize
self-attention along different dimensions, spatial or channel, and achieve
impressive performance. This inspires us to combine the two dimensions in
Transformer for a more powerful representation capability. Based on the above
idea, we propose a novel Transformer model, Dual Aggregation Transformer (DAT),
for image SR. Our DAT aggregates features across spatial and channel
dimensions, in the inter-block and intra-block dual manner. Specifically, we
alternately apply spatial and channel self-attention in consecutive Transformer
blocks. The alternate strategy enables DAT to capture the global context and
realize inter-block feature aggregation. Furthermore, we propose the adaptive
interaction module (AIM) and the spatial-gate feed-forward network (SGFN) to
achieve intra-block feature aggregation. AIM complements two self-attention
mechanisms from corresponding dimensions. Meanwhile, SGFN introduces additional
non-linear spatial information in the feed-forward network. Extensive
experiments show that our DAT surpasses current methods. Code and models are
obtainable at https://github.com/zhengchen1999/DAT.
