An efficient and effective person re-identification (ReID) system relieves
the users from painful and boring video watching and accelerates the process of
video analysis. Recently, with the explosive demands of practical applications,
a lot of research efforts have been dedicated to heterogeneous person
re-identification (Hetero-ReID). In this paper, we provide a comprehensive
review of state-of-the-art Hetero-ReID methods that address the challenge of
inter-modality discrepancies. According to the application scenario, we
classify the methods into four categories -- low-resolution, infrared, sketch,
and text. We begin with an introduction of ReID, and make a comparison between
Homogeneous ReID (Homo-ReID) and Hetero-ReID tasks. Then, we describe and
compare existing datasets for performing evaluations, and survey the models
that have been widely employed in Hetero-ReID. We also summarize and compare
the representative approaches from two perspectives, i.e., the application
scenario and the learning pipeline. We conclude by a discussion of some future
research directions. Follow-up updates are avaible at:
this https URL

本文综述了异构人员再识别中解决跨模态不一致性挑战的最新方法，根据应用场景将这些方法分为四个类别，描述和比较现有的数据集和模型，并从两个角度总结和比较代表性的方法，最后讨论了未来的研究方向。

异质人员再识别综述：超越单一模态

Beyond Intra-modality: A Survey of Heterogeneous Person  Re-identification

Persistence diagrams (PDs) play a key role in topological data analysis
(TDA), in which they are routinely used to describe topological properties of
complicated shapes. PDs enjoy strong stability properties and have proven their
utility in various learning contexts. They do not, however, live in a space
naturally endowed with a Hilbert structure and are usually compared with
specific distances, such as the bottleneck distance. To incorporate PDs in a
learning pipeline, several kernels have been proposed for PDs with a strong
emphasis on the stability of the RKHS distance w.r.t. perturbations of the PDs.
In this article, we use the Sliced Wasserstein approximation SW of the
Wasserstein distance to define a new kernel for PDs, which is not only provably
stable but also provably discriminative (depending on the number of points in
the PDs) w.r.t. the Wasserstein distance $d_1$ between PDs. We also demonstrate
its practicality, by developing an approximation technique to reduce kernel
computation time, and show that our proposal compares favorably to existing
kernels for PDs on several benchmarks.

本文提出了一种新的 persistence diagrams 的核函数，基于 Sliced Wasserstein approximation 方法，不仅具有稳定性，而且排斥性高，实用性强，并在多项基准测试中得到了验证。