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.

本文提出了一种基于金字塔的仿射回归网络 (PARN) 的深度体系结构，用于估计图像间的局部可变仿射变换场，实现在深度网络中粗到细的密集 affine 变换场估计。我们提出了一种新的弱监督训练方案，利用图像对之间的对应关系来生成渐进式监督，并将其应用于我们的 PARN 模型。尽管 PARN 仅在有限的监督下进行训练，但实验结果显示在各种基准测试中，PARN 的性能优于现有方法。