  Humans can easily recognize the importance of people in social event images,
and they always focus on the most important individuals. However, learning to
learn the relation between people in an image, and inferring the most important
person based on this relation, remains undeveloped. In this work, we propose a
deep imPOrtance relatIon NeTwork (POINT) that combines both relation modeling
and feature learning. In particular, we infer two types of interaction modules:
the person-person interaction module that learns the interaction between people
and the event-person interaction module that learns to describe how a person is
involved in the event occurring in an image. We then estimate the importance
relations among people from both interactions and encode the relation feature
from the importance relations. In this way, POINT automatically learns several
types of relation features in parallel, and we aggregate these relation
features and the person's feature to form the importance feature for important
people classification. Extensive experimental results show that our method is
effective for important people detection and verify the efficacy of learning to
learn relations for important people detection.
