Domain adaptation is often hampered by exceedingly small target datasets and
inaccessible source data. These conditions are prevalent in speech
verification, where privacy policies and/or languages with scarce speech
resources limit the availability of sufficient data. This paper explored
techniques of sourcefree domain adaptation unto a limited target speech dataset
for speaker verificationin data-scarce languages. Both language and channel
mis-match between source and target were investigated. Fine-tuning methods were
evaluated and compared across different sizes of labeled target data. A novel
iterative cluster-learn algorithm was studied for unlabeled target datasets.

本文研究了在语音验证中，源数据不可访问及目标数据集非常有限的情况下，无源领域适应技术的应用。同时研究了源数据与目标数据之间的语言和通道不匹配问题，并评估比较了不同大小标记目标数据集的微调方法，还对无标签目标数据集进行了基于迭代式聚类学习算法的研究。

无语言稀缺和噪音通道的免费领域适应话者验证

Source -Free Domain Adaptation for Speaker Verification in Data-Scarce  Languages and Noisy Channels

We unveil how generalizable AI can be used to improve multi-view 3D
pedestrian detection in unlabeled target scenes. One way to increase
generalization to new scenes is to automatically label target data, which can
then be used for training a detector model. In this context, we investigate two
approaches for automatically labeling target data: pseudo-labeling using a
supervised detector and automatic labeling using an untrained detector (that
can be applied out of the box without any training). We adopt a training
framework for optimizing detector models using automatic labeling procedures.
This framework encompasses different training sets/modes and multi-round
automatic labeling strategies. We conduct our analyses on the
publicly-available WILDTRACK and MultiviewX datasets. We show that, by using
the automatic labeling approach based on an untrained detector, we can obtain
superior results than directly using the untrained detector or a detector
trained with an existing labeled source dataset. It achieved a MODA about 4%
and 1% better than the best existing unlabeled method when using WILDTRACK and
MultiviewX as target datasets, respectively.

利用通用化人工智能改进无标签目标场景的多视角 3D 行人检测，通过使用未经训练的检测器的自动标注方法，可以获得比直接使用未经训练检测器或使用现有标记的源数据集训练的检测器更好的结果。在使用 WILDTRACK 和 MultiviewX 作为目标数据集时，MODA 比现有的无标签方法分别提高了 4% 和 1%。

面向可泛化人工智能的无标注多视角三维行人检测：技术和性能分析

Toward unlabeled multi-view 3D pedestrian detection by generalizable AI:  techniques and performance analysis

Rotated bounding boxes drastically reduce output ambiguity of elongated
objects, making it superior to axis-aligned bounding boxes. Despite the
effectiveness, rotated detectors are not widely employed. Annotating rotated
bounding boxes is such a laborious process that they are not provided in many
detection datasets where axis-aligned annotations are used instead. In this
paper, we propose a framework that allows the model to predict precise rotated
boxes only requiring cheaper axis-aligned annotation of the target dataset 1.
To achieve this, we leverage the fact that neural networks are capable of
learning richer representation of the target domain than what is utilized by
the task. The under-utilized representation can be exploited to address a more
detailed task. Our framework combines task knowledge of an out-of-domain source
dataset with stronger annotation and domain knowledge of the target dataset
with weaker annotation. A novel assignment process and projection loss are used
to enable the co-training on the source and target datasets. As a result, the
model is able to solve the more detailed task in the target domain, without
additional computation overhead during inference. We extensively evaluate the
method on various target datasets including fresh-produce dataset, HRSC2016 and
SSDD. Results show that the proposed method consistently performs on par with
the fully supervised approach.

本文提出一种基于共训练的方法，通过在目标数据集上进行更便宜的轴对齐注释，预测精确的旋转框，解决传统旋转检测器的旋转边界框注释耗时的问题。通过在跨域数据集上的知识转移，该方法可显著提高较弱注释数据集的学习效果，在各种目标数据集上表现良好。