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.
@article{arxiv.2106.11310,
title = {Towards Long-Form Video Understanding},
author = {Chao-Yuan Wu and Philipp Krähenbühl},
journal= {arXiv preprint arXiv:2106.11310},
year = {2021}
}