We present recurrent transformer networks (RTNs) for obtaining dense
correspondences between semantically similar images. Our networks accomplish
this through an iterative process of estimating spatial transformations between
the input images and using these transformations to generate aligned
convolutional activations. By directly estimating the transformations between
an image pair, rather than employing spatial transformer networks to
independently normalize each individual image, we show that greater accuracy
can be achieved. This process is conducted in a recursive manner to refine both
the transformation estimates and the feature representations. In addition, a
technique is presented for weakly-supervised training of RTNs that is based on
a proposed classification loss. With RTNs, state-of-the-art performance is
attained on several benchmarks for semantic correspondence.

本文提出了利用循环变压器网络（RTNs）对语义相似图像进行密集对应的方法，在迭代过程中估计图像间的空间变换，以及使用这些变换来生成对齐的卷积激活，通过直接估算两幅图像间的变换，而非独立正则化每个图像，我们证明了更高的精度可以得到，同时使用一种基于提出的分类损失的弱监督训练技术。利用 RTNs，在语义对应的多个基准测试中，实现了最先进的性能。