论文标题
转换器：语义通信的匹配对应
TransforMatcher: Match-to-Match Attention for Semantic Correspondence
论文作者
论文摘要
建立图像之间的对应关系仍然是一项具有挑战性的任务，尤其是由于不同的观点或类内变化而导致的外观变化。在这项工作中，我们介绍了一个强大的语义图像匹配者，即“变形金组”（Transformatcher），该学习者基于视觉域中变形金刚网络的成功。与对应关系的现有卷积或基于注意力的方案不同，Transformatcher对精确的匹配定位和动态改进执行全局匹配的关注。为了在密集的相关图中处理大量匹配，我们开发了一个轻量级的注意体系结构，以考虑全局匹配匹配的交互。我们还建议利用多通道相关图进行改进，将多级别分数视为特征，而不是单个分数来完全利用较富裕的层次语义。在实验中，变形金组在与PF-Pascal数据集上的现有SOTA方法相当的同时，在SPAIR-71K上设置了新的最新状态。
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.
