  Our world offers a never-ending stream of visual stimuli, yet today's vision
systems only accurately recognize patterns within a few seconds. These systems
understand the present, but fail to contextualize it in past or future events.
In this paper, we study long-form video understanding. We introduce a framework
for modeling long-form videos and develop evaluation protocols on large-scale
datasets. We show that existing state-of-the-art short-term models are limited
for long-form tasks. A novel object-centric transformer-based video recognition
architecture performs significantly better on 7 diverse tasks. It also
outperforms comparable state-of-the-art on the AVA dataset.
