Military drones are taking an increasingly prominent role in armed conflict,
and the use of multiple drones in a swarm can be useful. Who the drivers of the
research are and what sub-domains exist is analyzed and visually presented in
this research using NLP techniques based on 946 studies. Most research is
conducted in the Western world, led by the United States, the United Kingdom,
and Germany. Through Tf-idf scoring, it is shown that countries have
significant differences in the subdomains studied. Overall, 2019 and 2020 saw
the most works published, with significant interest in military swarm drones as
early as 2008. This study provides a first glimpse into research in this area
and prompts further investigation.

通过 NLP 技术对 946 篇研究进行分析和可视化呈现，发现军事无人机和群智能技术是该领域的主要研究方向，其中美国、英国和德国等西方国家的研究较为领先，全球各国在子领域的研究也存在显著差异，研究热度自 2008 年以来不断攀升，该研究对未来在这一领域的深入研究提供了初步的参考。

自然语言处理分析军用群体无人机的发展驱动因素

Analyse der Entwicklungstreiber militärischer Schwarmdrohnen durch Natural Language Processing

Cross-domain transfer learning (CDTL) is an extremely challenging task for
the person re-identification (ReID). Given a source domain with annotations and
a target domain without annotations, CDTL seeks an effective method to transfer
the knowledge from the source domain to the target domain. However, such a
simple two-domain transfer learning method is unavailable for the person ReID
in that the source/target domain consists of several sub-domains, e.g.,
camera-based sub-domains. To address this intractable problem, we propose a
novel Many-to-Many Generative Adversarial Transfer Learning method (M2M-GAN)
that takes multiple source sub-domains and multiple target sub-domains into
consideration and performs each sub-domain transferring mapping from the source
domain to the target domain in a unified optimization process. The proposed
method first translates the image styles of source sub-domains into that of
target sub-domains, and then performs the supervised learning by using the
transferred images and the corresponding annotations in source domain. As the
gap is reduced, M2M-GAN achieves a promising result for the cross-domain person
ReID. Experimental results on three benchmark datasets Market-1501,
DukeMTMC-reID and MSMT17 show the effectiveness of our M2M-GAN.

针对多相机场景下的行人再识别问题，本文提出了一种考虑多个子域之间映射关系的多对多生成对抗迁移学习方法 (M2M-GAN)，该方法首先进行图像风格的转换，然后应用转换后的图像和源域的注释信息进行监督学习，实验结果表明 M2M-GAN 方法具有很好的泛化能力和鲁棒性，且在 Market-1501, DukeMTMC-reID 和 MSMT17 数据集上均取得了良好的效果。