  Although human action anticipation is a task which is inherently multi-modal,
state-of-the-art methods on well known action anticipation datasets leverage
this data by applying ensemble methods and averaging scores of unimodal
anticipation networks. In this work we introduce transformer based modality
fusion techniques, which unify multi-modal data at an early stage. Our
Anticipative Feature Fusion Transformer (AFFT) proves to be superior to popular
score fusion approaches and presents state-of-the-art results outperforming
previous methods on EpicKitchens-100 and EGTEA Gaze+. Our model is easily
extensible and allows for adding new modalities without architectural changes.
Consequently, we extracted audio features on EpicKitchens-100 which we add to
the set of commonly used features in the community.
