TransforMatcher: Match-to-Match Attention for Semantic Correspondence. (arXiv:2205.11634v1 [cs.CV])

Establishing correspondences between images remains a challenging task,
especially under large appearance changes due to different viewpoints or
intra-class variations. In this work, we introduce a strong semantic image
matching learner, dubbed TransforMatcher, which builds on the success of
transformer networks in vision domains. Unlike existing convolution- or
attention-based schemes for correspondence, TransforMatcher performs global
match-to-match attention for precise match localization and dynamic refinement.
To handle a large number of matches in a dense correlation map, we develop a
light-weight …

arxiv attention cv semantic