Papers › Temporal Aggregate Representations for Long-Range Video Understanding

Temporal Aggregate Representations for Long-Range Video Understanding

1 Jun 2020ECCV 2020 8arXiv:2006.00830archive 2025-07-28

Fadime Sener, Dipika Singhania, Angela Yao

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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