CATs++: Boosting Cost Aggregation with Convolutions and Transformers

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.