The problem of human activity recognition from mobile sensor data applies to
multiple domains, such as health monitoring, personal fitness, daily life
logging, and senior care. A critical challenge for training human activity
recognition models is data quality. Acquiring balanced datasets containing
accurate activity labels requires humans to correctly annotate and potentially
interfere with the subjects' normal activities in real-time. Despite the
likelihood of incorrect annotation or lack thereof, there is often an inherent
chronology to human behavior. For example, we take a shower after we exercise.
This implicit chronology can be used to learn unknown labels and classify
future activities. In this work, we propose HAR-GCCN, a deep graph CNN model
that leverages the correlation between chronologically adjacent sensor
measurements to predict the correct labels for unclassified activities that
have at least one activity label. We propose a new training strategy enforcing
that the model predicts the missing activity labels by leveraging the known
ones. HAR-GCCN shows superior performance relative to previously used baseline
methods, improving classification accuracy by about 25% and up to 68% on
different datasets. Code is available at
https://github.com/abduallahmohamed/HAR-GCNN.

提出了一种基于深度图卷积神经网络的人体活动识别方法（HAR-GCCN），该方法利用传感器测量之间的时间相邻性来预测未分类活动的正确标签。通过新的训练策略和数据集，该方法在多个数据集上展现了比现有基线方法优越约 25％和高达 68％的分类准确性。

HAR-GCNN: 利用深度图卷积神经网络从高度未标记的移动传感器数据中识别人类活动

HAR-GCNN: Deep Graph CNNs for Human Activity Recognition From Highly Unlabeled Mobile Sensor Data

A major challenge for physically unconstrained gaze estimation is acquiring
training data with 3D gaze annotations for in-the-wild and outdoor scenarios.
In contrast, videos of human interactions in unconstrained environments are
abundantly available and can be much more easily annotated with frame-level
activity labels. In this work, we tackle the previously unexplored problem of
weakly-supervised gaze estimation from videos of human interactions. We
leverage the insight that strong gaze-related geometric constraints exist when
people perform the activity of "looking at each other" (LAEO). To acquire
viable 3D gaze supervision from LAEO labels, we propose a training algorithm
along with several novel loss functions especially designed for the task. With
weak supervision from two large scale CMU-Panoptic and AVA-LAEO activity
datasets, we show significant improvements in (a) the accuracy of
semi-supervised gaze estimation and (b) cross-domain generalization on the
state-of-the-art physically unconstrained in-the-wild Gaze360 gaze estimation
benchmark. We open source our code at
this https URL

本研究针对自然场景下获取三维凝视注释的挑战进行了探讨，提出了一种基于 LAEO 标签的弱监督凝视估计算法，该算法通过训练算法和损失函数实现了可行的三维凝视监督，结果表明，这种方法对于半监督凝视估计的准确性和跨域泛化的改进方面取得了显着的进展。