  Strongly supervised learning requires detailed knowledge of truth labels at
instance levels, and in many machine learning applications this is a major
drawback. Multiple instance learning (MIL) is a popular weakly supervised
learning method where truth labels are not available at instance level, but
only at bag-of-instances level. However, sometimes the nature of the problem
requires a more complex description, where a nested architecture of bag-of-bags
at different levels can capture underlying relationships, like similar
instances grouped together. Predicting the latent labels of instances or
inner-bags might be as important as predicting the final bag-of-bags label but
is lost in a straightforward nested setting. We propose a Nested Multiple
Instance with Attention (NMIA) model architecture combining the concept of
nesting with attention mechanisms. We show that NMIA performs as conventional
MIL in simple scenarios and can grasp a complex scenario providing insights to
the latent labels at different levels.
