When creating multi-channel time-series datasets for Human Activity
Recognition (HAR), researchers are faced with the issue of subject selection
criteria. It is unknown what physical characteristics and/or soft-biometrics,
such as age, height, and weight, need to be taken into account to train a
classifier to achieve robustness towards heterogeneous populations in the
training and testing data. This contribution statistically curates the training
data to assess to what degree the physical characteristics of humans influence
HAR performance. We evaluate the performance of a state-of-the-art
convolutional neural network on two HAR datasets that vary in the sensors,
activities, and recording for time-series HAR. The training data is
intentionally biased with respect to human characteristics to determine the
features that impact motion behaviour. The evaluations brought forth the impact
of the subjects' characteristics on HAR. Thus, providing insights regarding the
robustness of the classifier with respect to heterogeneous populations. The
study is a step forward in the direction of fair and trustworthy artificial
intelligence by attempting to quantify representation bias in multi-channel
time series HAR data.

本文旨在评估人类身体特征对多通道时间序列 HAR 数据的影响，并试图量化表示偏差，以提供公正和可信的人工智能。

人体活动识别中的数据集偏差

Dataset Bias in Human Activity Recognition

Soft-biometrics play an important role in face biometrics and related fields
since these might lead to biased performances, threatens the user's privacy, or
are valuable for commercial aspects. Current face databases are specifically
constructed for the development of face recognition applications. Consequently,
these databases contain large amount of face images but lack in the number of
attribute annotations and the overall annotation correctness. In this work, we
propose MAADFace, a new face annotations database that is characterized by the
large number of its high-quality attribute annotations. MAADFace is build on
the VGGFace2 database and thus, consists of 3.3M faces of over 9k individuals.
Using a novel annotation transfer-pipeline that allows an accurate
label-transfer from multiple source-datasets to a target-dataset, MAAD-Face
consists of 123.9M attribute annotations of 47 different binary attributes.
Consequently, it provides 15 and 137 times more attribute labels than CelebA
and LFW. Our investigation on the annotation quality by three human evaluators
demonstrated the superiority of the MAAD-Face annotations over existing
databases. Additionally, we make use of the large amount of high-quality
annotations from MAAD-Face to study the viability of soft-biometrics for
recognition, providing insights about which attributes support genuine and
imposter decisions. The MAAD-Face annotations dataset is publicly available.

本文介绍了一个基于 VGGFace2 数据集构建的被称为 MAADFace 的人脸注释数据库，该数据库包含超过 9k 个人的 3.3M 张脸，总共包含 47 个外貌属性标记，提供了比 CelebA 和 LFW 多 15 到 137 倍的属性标签，并探讨了软生物特征识别的可行性和有关属性对真伪决策的支持。

MAAD-Face: 用于人脸图像的大规模属性注释数据集

MAAD-Face: A Massively Annotated Attribute Dataset for Face Images

Person re-identification (Re-ID) aims to match a target person across camera
views at different locations and times. Existing Re-ID studies focus on the
short-term cloth-consistent setting, under which a person re-appears in
different camera views with the same outfit. A discriminative feature
representation learned by existing deep Re-ID models is thus dominated by the
visual appearance of clothing. In this work, we focus on a much more difficult
yet practical setting where person matching is conducted over long-duration,
e.g., over days and months and therefore inevitably under the new challenge of
changing clothes. This problem, termed Long-Term Cloth-Changing (LTCC) Re-ID is
much understudied due to the lack of large scale datasets. The first
contribution of this work is a new LTCC dataset containing people captured over
a long period of time with frequent clothing changes. As a second contribution,
we propose a novel Re-ID method specifically designed to address the
cloth-changing challenge. Specifically, we consider that under cloth-changes,
soft-biometrics such as body shape would be more reliable. We, therefore,
introduce a shape embedding module as well as a cloth-elimination
shape-distillation module aiming to eliminate the now unreliable clothing
appearance features and focus on the body shape information. Extensive
experiments show that superior performance is achieved by the proposed model on
the new LTCC dataset. The code and dataset will be available at
this https URL

本文介绍了一个新的级联网络，该网络可以在不同场景下实现个人再识别，它不仅在衣服外观发生变化的情况下进行操作，而且还利用了 “soft-biometrics” 的概念，它包括身体形态等特征来增强匹配的准确性。

长期换衣服个体重识别

Long-Term Cloth-Changing Person Re-identification

Visual attributes, from simple objects (e.g., backpacks, hats) to
soft-biometrics (e.g., gender, height, clothing) have proven to be a powerful
representational approach for many applications such as image description and
human identification. In this paper, we introduce a novel method to combine the
advantages of both multi-task and curriculum learning in a visual attribute
classification framework. Individual tasks are grouped based on their
correlation so that two groups of strongly and weakly correlated tasks are
formed. The two groups of tasks are learned in a curriculum learning setup by
transferring the acquired knowledge from the strongly to the weakly correlated.
The learning process within each group though, is performed in a multi-task
classification setup. The proposed method learns better and converges faster
than learning all the tasks in a typical multi-task learning paradigm. We
demonstrate the effectiveness of our approach on the publicly available, SoBiR,
VIPeR and PETA datasets and report state-of-the-art results across the board.

介绍了一种新颖的方法，在视觉属性分类框架中结合多任务和课程学习的优点，在各个任务之间迁移知识来提高学习速度，该方法在公开数据集上取得了最新的表现。