  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.
