  Cost aggregation is a highly important process in image matching tasks, which
aims to disambiguate the noisy matching scores. Existing methods generally
tackle this by hand-crafted or CNN-based methods, which either lack robustness
to severe deformations or inherit the limitation of CNNs that fail to
discriminate incorrect matches due to limited receptive fields and
inadaptability. In this paper, we introduce Cost Aggregation with Transformers
(CATs) to tackle this by exploring global consensus among initial correlation
map with the help of some architectural designs that allow us to fully enjoy
global receptive fields of self-attention mechanism. Also, to alleviate some of
the limitations that CATs may face, i.e., high computational costs induced by
the use of a standard transformer that its complexity grows with the size of
spatial and feature dimensions, which restrict its applicability only at
limited resolution and result in rather limited performance, we propose CATs++,
an extension of CATs. Our proposed methods outperform the previous
state-of-the-art methods by large margins, setting a new state-of-the-art for
all the benchmarks, including PF-WILLOW, PF-PASCAL, and SPair-71k. We further
provide extensive ablation studies and analyses.
