  Anticipating actions before they are executed is crucial for a wide range of
practical applications, including autonomous driving and robotics. In this
paper, we study the egocentric action anticipation task, which predicts future
action seconds before it is performed for egocentric videos. Previous
approaches focus on summarizing the observed content and directly predicting
future action based on past observations. We believe it would benefit the
action anticipation if we could mine some cues to compensate for the missing
information of the unobserved frames. We then propose to decompose the action
anticipation into a series of future feature predictions. We imagine how the
visual feature changes in the near future and then predicts future action
labels based on these imagined representations. Differently, our ImagineRNN is
optimized in a contrastive learning way instead of feature regression. We
utilize a proxy task to train the ImagineRNN, i.e., selecting the correct
future states from distractors. We further improve ImagineRNN by residual
anticipation, i.e., changing its target to predicting the feature difference of
adjacent frames instead of the frame content. This promotes the network to
focus on our target, i.e., the future action, as the difference between
adjacent frame features is more important for forecasting the future. Extensive
experiments on two large-scale egocentric action datasets validate the
effectiveness of our method. Our method significantly outperforms previous
methods on both the seen test set and the unseen test set of the EPIC Kitchens
Action Anticipation Challenge.
