In supervised learning, low quality annotations lead to poorly performing classification and detection models, while also rendering evaluation unreliable. This is particularly apparent on temporal data, where annotation quality is affected by multiple factors. For example, in the post-hoc self-reporting of daily activities, cognitive biases are one of the most common ingredients. In particular, reporting the start and duration of an activity after its finalisation may incorporate biases introduced by personal time perceptions, as well as the imprecision and lack of granularity due to time rounding. Here we propose a method to model human biases on temporal annotations and argue for the use of soft labels. Experimental results in synthetic data show that soft labels provide a better approximation of the ground truth for several metrics. We showcase the method on a real dataset of daily activities.

翻译：在监督学习中，低质量标注会导致分类和检测模型性能差，同时使评估不可靠。这一问题在时间序列数据上尤为明显，因为标注质量受多种因素影响。例如，在日常活动的回溯性自我报告中，认知偏差是最常见的因素之一。具体而言，在活动结束后报告其开始时间和持续时间，可能引入个人时间感知带来的偏差，以及时间取整导致的不精确性和粒度缺失。本文提出一种对人类时间标注偏差进行建模的方法，并主张采用软标签。合成数据实验结果表明，软标签能在多项指标上更好地逼近真实标注。我们还在一个真实日常活动数据集上展示了该方法的有效性。