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 attention architecture to consider the global match-to-match
interactions. We also propose to utilize a multi-channel correlation map for
refinement, treating the multi-level scores as features instead of a single
score to fully exploit the richer layer-wise semantics. In experiments,
TransforMatcher sets a new state of the art on SPair-71k while performing on
par with existing SOTA methods on the PF-PASCAL dataset.

本文介绍了基于 Transformer 网络的图像语义匹配学习器 TransforMatcher，通过全局匹配 - 匹配注意力和动态细化，利用轻量级注意力架构实现匹配处理和基于多层次特征的多通道相关图的精细化处理，以达到确切的匹配定位和 Semantic Learning 的效果