Temporal Aggregate Representations for Long-Range Video Understanding

• URL: http://arxiv.org/abs/2006.00830v2
• Date: Thu, 30 Jul 2020 23:33:43 GMT
• Title: Temporal Aggregate Representations for Long-Range Video Understanding
• Authors: Fadime Sener and Dipika Singhania and Angela Yao
• Abstract summary: Future prediction, especially in long-range videos, requires reasoning from current and past observations. We address questions of temporal extent, scaling, and level of semantic abstraction with a flexible multi-granular temporal aggregation framework.
• Score: 26.091400303122867
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Future prediction, especially in long-range videos, requires reasoning from current and past observations. In this work, we address questions of temporal extent, scaling, and level of semantic abstraction with a flexible multi-granular temporal aggregation framework. We show that it is possible to achieve state of the art in both next action and dense anticipation with simple techniques such as max-pooling and attention. To demonstrate the anticipation capabilities of our model, we conduct experiments on Breakfast, 50Salads, and EPIC-Kitchens datasets, where we achieve state-of-the-art results. With minimal modifications, our model can also be extended for video segmentation and action recognition.

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