CATs++: Boosting Cost Aggregation with Convolutions and Transformers. (arXiv:2202.06817v2 [cs.CV] UPDATED)

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 …

aggregation arxiv boosting cats cost transformers