  Multi-scale learning is central to semantic segmentation. We visualize the
effective receptive field (ERF) of canonical multi-scale representations and
point out two risks in learning them: scale inadequacy and field inactivation.
A novel multi-scale learner, varying window attention (VWA), is presented to
address these issues. VWA leverages the local window attention (LWA) and
disentangles LWA into the query window and context window, allowing the
context's scale to vary for the query to learn representations at multiple
scales. However, varying the context to large-scale windows (enlarging ratio R)
can significantly increase the memory footprint and computation cost (R^2 times
larger than LWA). We propose a simple but professional re-scaling strategy to
zero the extra induced cost without compromising performance. Consequently, VWA
uses the same cost as LWA to overcome the receptive limitation of the local
window. Furthermore, depending on VWA and employing various MLPs, we introduce
a multi-scale decoder (MSD), VWFormer, to improve multi-scale representations
for semantic segmentation. VWFormer achieves efficiency competitive with the
most compute-friendly MSDs, like FPN and MLP decoder, but performs much better
than any MSDs. For instance, using nearly half of UPerNet's computation,
VWFormer outperforms it by 1.0%-2.5% mIoU on ADE20K. With little extra
overhead, ~10G FLOPs, Mask2Former armed with VWFormer improves by 1.0%-1.3%.
The code and models are available at https://github.com/yan-hao-tian/vw
