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。

PARN: 金字塔仿射回归网络用于密集语义对应

PARN: Pyramidal Affine Regression Networks for Dense Semantic  Correspondence

Techniques for dense semantic correspondence have provided limited ability to
deal with the geometric variations that commonly exist between semantically
similar images. While variations due to scale and rotation have been examined,
there lack practical solutions for more complex deformations such as affine
transformations because of the tremendous size of the associated solution
space. To address this problem, we present a discrete-continuous transformation
matching (DCTM) framework where dense affine transformation fields are inferred
through a discrete label optimization in which the labels are iteratively
updated via continuous regularization. In this way, our approach draws
solutions from the continuous space of affine transformations in a manner that
can be computed efficiently through constant-time edge-aware filtering and a
proposed affine-varying CNN-based descriptor. Experimental results show that
this model outperforms the state-of-the-art methods for dense semantic
correspondence on various benchmarks.

通过离散 - 连续变换匹配框架中的迭代标签优化和连续正则化，提出了一种基于卷积神经网络描述符的密集仿射变换场推测方法，能够高效地从仿射变换的连续空间中得到解决方案。实验结果表明，该模型在各种基准测试中优于现有的密集语义对应方法。

DCTM：离散 - 连续变换匹配在语义流中的应用

DCTM: Discrete-Continuous Transformation Matching for Semantic Flow

We present a descriptor, called fully convolutional self-similarity (FCSS),
for dense semantic correspondence. To robustly match points among different
instances within the same object class, we formulate FCSS using local
self-similarity (LSS) within a fully convolutional network. In contrast to
existing CNN-based descriptors, FCSS is inherently insensitive to intra-class
appearance variations because of its LSS-based structure, while maintaining the
precise localization ability of deep neural networks. The sampling patterns of
local structure and the self-similarity measure are jointly learned within the
proposed network in an end-to-end and multi-scale manner. As training data for
semantic correspondence is rather limited, we propose to leverage object
candidate priors provided in existing image datasets and also correspondence
consistency between object pairs to enable weakly-supervised learning.
Experiments demonstrate that FCSS outperforms conventional handcrafted
descriptors and CNN-based descriptors on various benchmarks.

本研究提出了一种基于全卷积自相似性（FCSS）的描述符，用于密集的语义对应。与现有的基于 CNN 的描述符不同，FCSS 是因其 LSS-based 结构本质上不敏感于类内外观变化，同时保持了深度神经网络的精确定位能力。通过在拟议网络中以端到端和多尺度的方式联合学习局部结构的抽样模式和自相似性度量。本研究建议利用现有图像数据集中提供的对象候选先验和对象对之间的一致性，以实现弱监督学习。实验表明，在各种基准测试中，FCSS 胜过传统手工制作的描述符和基于 CNN 的描述符。