论文标题
监督水平量表（SLS）
Supervision Levels Scale (SLS)
论文作者
论文摘要
我们提出了三维离散和增量量表，以编码方法的监督级别 - 即训练模型时使用的数据和标签以实现给定的性能。我们捕获了监督的三个方面，这些方面众所周知，它们在需要额外成本的同时为方法提供了优势：预培训，培训标签和培训数据。提出的三维量表可以包括在结果表或排行榜中，不仅可以通过其性能，而且还通过每种方法使用的数据监督级别进行比较。首先在任何任务/数据集/挑战中首先提出监督水平量表（SLS）。然后将其应用于Epic-Kitchens-100数据集，用于与此数据集相关的各种排行榜和挑战。
We propose a three-dimensional discrete and incremental scale to encode a method's level of supervision - i.e. the data and labels used when training a model to achieve a given performance. We capture three aspects of supervision, that are known to give methods an advantage while requiring additional costs: pre-training, training labels and training data. The proposed three-dimensional scale can be included in result tables or leaderboards to handily compare methods not only by their performance, but also by the level of data supervision utilised by each method. The Supervision Levels Scale (SLS) is first presented generally fo any task/dataset/challenge. It is then applied to the EPIC-KITCHENS-100 dataset, to be used for the various leaderboards and challenges associated with this dataset.
