  This paper presents a deep architecture for dense semantic correspondence,
called pyramidal affine regression networks (PARN), that estimates
locally-varying affine transformation fields across images. To deal with
intra-class appearance and shape variations that commonly exist among different
instances within the same object category, we leverage a pyramidal model where
affine transformation fields are progressively estimated in a coarse-to-fine
manner so that the smoothness constraint is naturally imposed within deep
networks. PARN estimates residual affine transformations at each level and
composes them to estimate final affine transformations. Furthermore, to
overcome the limitations of insufficient training data for semantic
correspondence, we propose a novel weakly-supervised training scheme that
generates progressive supervisions by leveraging a correspondence consistency
across image pairs. Our method is fully learnable in an end-to-end manner and
does not require quantizing infinite continuous affine transformation fields.
To the best of our knowledge, it is the first work that attempts to estimate
dense affine transformation fields in a coarse-to-fine manner within deep
networks. Experimental results demonstrate that PARN outperforms the
state-of-the-art methods for dense semantic correspondence on various
benchmarks.
