  The 3rd annual installment of the ActivityNet Large- Scale Activity
Recognition Challenge, held as a full-day workshop in CVPR 2018, focused on the
recognition of daily life, high-level, goal-oriented activities from
user-generated videos as those found in internet video portals. The 2018
challenge hosted six diverse tasks which aimed to push the limits of semantic
visual understanding of videos as well as bridge visual content with human
captions. Three out of the six tasks were based on the ActivityNet dataset,
which was introduced in CVPR 2015 and organized hierarchically in a semantic
taxonomy. These tasks focused on tracing evidence of activities in time in the
form of proposals, class labels, and captions. In this installment of the
challenge, we hosted three guest tasks to enrich the understanding of visual
information in videos. The guest tasks focused on complementary aspects of the
activity recognition problem at large scale and involved three challenging and
recently compiled datasets: the Kinetics-600 dataset from Google DeepMind, the
AVA dataset from Berkeley and Google, and the Moments in Time dataset from MIT
and IBM Research.
