Long-term visual localization is the problem of estimating the camera pose of
a given query image in a scene whose appearance changes over time. It is an
important problem in practice, for example, encountered in autonomous driving.
In order to gain robustness to such changes, long-term localization approaches
often use segmantic segmentations as an invariant scene representation, as the
semantic meaning of each scene part should not be affected by seasonal and
other changes. However, these representations are typically not very
discriminative due to the limited number of available classes. In this paper,
we propose a new neural network, the Fine-Grained Segmentation Network (FGSN),
that can be used to provide image segmentations with a larger number of labels
and can be trained in a self-supervised fashion. In addition, we show how FGSNs
can be trained to output consistent labels across seasonal changes. We
demonstrate through extensive experiments that integrating the fine-grained
segmentations produced by our FGSNs into existing localization algorithms leads
to substantial improvements in localization performance.

本文研究长期视觉定位问题，提出了 Fine-Grained Segmentation Network（FGSN）用于提供更多标签的图像分割，并能自我学习以应对季节性变化，实验证明将 FGSN 的细粒度分割整合到现有的定位算法中可显著提高定位精度。

细粒度分割网络：自监督分割以提高长期视觉定位

Fine-Grained Segmentation Networks: Self-Supervised Segmentation for  Improved Long-Term Visual Localization

This paper addresses the problem of determining dense pixel correspondences
between two images and its application to geometric correspondence verification
in image retrieval. The main contribution is a geometric correspondence
verification approach for re-ranking a shortlist of retrieved database images
based on their dense pair-wise matching with the query image at a pixel level.
We determine a set of cyclically consistent dense pixel matches between the
pair of images and evaluate local similarity of matched pixels using neural
network based image descriptors. Final re-ranking is based on a novel
similarity function, which fuses the local similarity metric with a global
similarity metric and a geometric consistency measure computed for the matched
pixels. For dense matching our approach utilizes a modified version of a
recently proposed dense geometric correspondence network (DGC-Net), which we
also improve by optimizing the architecture. The proposed model and similarity
metric compare favourably to the state-of-the-art image retrieval methods. In
addition, we apply our method to the problem of long-term visual localization
demonstrating promising results and generalization across datasets.

本文针对在图像检索中几何对应验证，提出了以像素级密集配对检索数据库图像并以全局相似度度量和匹配像素计算的几何一致性度量为基础进行短序列重排的新模型，并通过实验证明了该模型具有竞争力。此外，将该方法应用于长期视觉本地化问题，并在跨数据集上进行了验证。