  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.
