We propose a new approach to determine correspondences between image pairs in
the wild under large changes in illumination, viewpoint, context, and material.
While other approaches find correspondences between pairs of images by treating
the images independently, we instead condition on both images to implicitly
take account of the differences between them. To achieve this, we introduce (i)
a spatial attention mechanism (a co-attention module, CoAM) for conditioning
the learned features on both images, and (ii) a distinctiveness score used to
choose the best matches at test time. CoAM can be added to standard
architectures and trained using self-supervision or supervised data, and
achieves a significant performance improvement under hard conditions, e.g.
large viewpoint changes. We demonstrate that models using CoAM achieve state of
the art or competitive results on a wide range of tasks: local matching, camera
localization, 3D reconstruction, and image stylization.

提出了一种新的方法来确定自然环境下图像对之间的对应关系，在大的光照变化、视角变化、语境和材料下进行。该方法引入了一种空间注意力机制和一个独特性得分，能够改善对这些因素变化下的对应关系的检测，并在许多任务中实现了最先进或具有竞争力的结果。

条件图像匹配的协同关注

Co-Attention for Conditioned Image Matching

We present semantic attribute matching networks (SAM-Net) for jointly
establishing correspondences and transferring attributes across semantically
similar images, which intelligently weaves the advantages of the two tasks
while overcoming their limitations. SAM-Net accomplishes this through an
iterative process of establishing reliable correspondences by reducing the
attribute discrepancy between the images and synthesizing attribute transferred
images using the learned correspondences. To learn the networks using weak
supervisions in the form of image pairs, we present a semantic attribute
matching loss based on the matching similarity between an attribute transferred
source feature and a warped target feature. With SAM-Net, the state-of-the-art
performance is attained on several benchmarks for semantic matching and
attribute transfer.

本论文提出了一种基于语义属性匹配网络的方法（SAM-Net），通过迭代式地建立图像之间的可靠对应关系和综合属性转移来智能地融合两个任务的优点并克服它们的局限性，并采用基于语义属性匹配的损失函数利用弱监督实现网络的学习。我们在几个语义匹配和属性转移的基准测试中取得了最先进的性能表现。

语义属性匹配网络

Semantic Attribute Matching Networks

The estimation of correspondences between two images resp. point sets is a
core problem in computer vision. One way to formulate the problem is graph
matching leading to the quadratic assignment problem which is NP-hard. Several
so called second order methods have been proposed to solve this problem. In
recent years hypergraph matching leading to a third order problem became
popular as it allows for better integration of geometric information. For most
of these third order algorithms no theoretical guarantees are known. In this
paper we propose a general framework for tensor block coordinate ascent methods
for hypergraph matching. We propose two algorithms which both come along with
the guarantee of monotonic ascent in the matching score on the set of discrete
assignment matrices. In the experiments we show that our new algorithms
outperform previous work both in terms of achieving better matching scores and
matching accuracy. This holds in particular for very challenging settings where
one has a high number of outliers and other forms of noise.

本文提出了一种新的超图匹配框架，使用张量块协同升级方法，以排除离群值和其他噪音形式，改善超图匹配的成绩和准确性。