Towards Long-Form Video Understanding

• URL: http://arxiv.org/abs/2106.11310v1
• Date: Mon, 21 Jun 2021 17:59:52 GMT
• Title: Towards Long-Form Video Understanding
• Authors: Chao-Yuan Wu, Philipp Kr\"ahenb\"uhl
• Abstract summary: We introduce a framework for modeling long-form videos and develop evaluation protocols on large-scale datasets. A novel object-centric transformer-based video recognition architecture performs significantly better on 7 diverse tasks.
• Score: 7.962725903399016
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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