Wearable technologies enable continuous monitoring of various health metrics,
such as physical activity, heart rate, sleep, and stress levels. A key
challenge with wearable data is obtaining quality labels. Unlike modalities
like video where the videos themselves can be effectively used to label objects
or events, wearable data do not contain obvious cues about the physical
manifestation of the users and usually require rich metadata. As a result,
label noise can become an increasingly thorny issue when labeling such data. In
this paper, we propose a novel solution to address noisy label learning,
entitled Few-Shot Human-in-the-Loop Refinement (FHLR). Our method initially
learns a seed model using weak labels. Next, it fine-tunes the seed model using
a handful of expert corrections. Finally, it achieves better generalizability
and robustness by merging the seed and fine-tuned models via weighted parameter
averaging. We evaluate our approach on four challenging tasks and datasets, and
compare it against eight competitive baselines designed to deal with noisy
labels. We show that FHLR achieves significantly better performance when
learning from noisy labels and achieves state-of-the-art by a large margin,
with up to 19% accuracy improvement under symmetric and asymmetric noise.
Notably, we find that FHLR is particularly robust to increased label noise,
unlike prior works that suffer from severe performance degradation. Our work
not only achieves better generalization in high-stakes health sensing
benchmarks but also sheds light on how noise affects commonly-used models.

利用佩戴式技术进行健康监测时，提出了 Few-Shot Human-in-the-Loop Refinement（FHLR）方法，通过使用弱标签和专家修正，结合加权参数平均，实现了从噪声标签学习的改进，并在健康感知基准测试中取得了较好的性能。

通过少量人机交互改进学习在标签噪声下的表现

Learning under Label Noise through Few-Shot Human-in-the-Loop Refinement

The emergence of new wearable technologies such as action cameras and
smart-glasses has increased the interest of computer vision scientists in the
First Person perspective. Nowadays, this field is attracting attention and
investments of companies aiming to develop commercial devices with First Person
Vision recording capabilities. Due to this interest, an increasing demand of
methods to process these videos, possibly in real-time, is expected. Current
approaches present a particular combinations of different image features and
quantitative methods to accomplish specific objectives like object detection,
activity recognition, user machine interaction and so on. This paper summarizes
the evolution of the state of the art in First Person Vision video analysis
between 1997 and 2014, highlighting, among others, most commonly used features,
methods, challenges and opportunities within the field.

介绍第一人称视角感知技术，重点是对于视频分析的实时处理方案的发展历程，方法及挑战。