  Visual engagement in social media platforms comprises interactions with photo
posts including comments, shares, and likes. In this paper, we leverage such
visual engagement clues as supervisory signals for representation learning.
However, learning from engagement signals is non-trivial as it is not clear how
to bridge the gap between low-level visual information and high-level social
interactions. We present VisE, a weakly supervised learning approach, which
maps social images to pseudo labels derived by clustered engagement signals. We
then study how models trained in this way benefit subjective downstream
computer vision tasks such as emotion recognition or political bias detection.
Through extensive studies, we empirically demonstrate the effectiveness of VisE
across a diverse set of classification tasks beyond the scope of conventional
recognition.
