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Action anticipation and forecasting in videos do not require a hat-trick, asfar as there are signs in the context to foresee how actions are going to bedeployed. Capturing these signs is hard because the context includes the past.We propose an end-to-end network for action anticipation and forecasting withmemory, to both anticipate the current action and foresee the next one.Experiments on action sequence datasets show excellent results indicating thattraining on histories with a dynamic memory can significantly improveforecasting performance.

Anticipation and next action forecasting in video: an end-to-end model with memory