  Objects are crucial for understanding human-object interactions. By
identifying the relevant objects, one can also predict potential future
interactions or actions that may occur with these objects. In this paper, we
study the problem of Short-Term Object interaction anticipation (STA) and
propose NAOGAT (Next-Active-Object Guided Anticipation Transformer), a
multi-modal end-to-end transformer network, that attends to objects in observed
frames in order to anticipate the next-active-object (NAO) and, eventually, to
guide the model to predict context-aware future actions. The task is
challenging since it requires anticipating future action along with the object
with which the action occurs and the time after which the interaction will
begin, a.k.a. the time to contact (TTC). Compared to existing video modeling
architectures for action anticipation, NAOGAT captures the relationship between
objects and the global scene context in order to predict detections for the
next active object and anticipate relevant future actions given these
detections, leveraging the objects' dynamics to improve accuracy. One of the
key strengths of our approach, in fact, is its ability to exploit the motion
dynamics of objects within a given clip, which is often ignored by other
models, and separately decoding the object-centric and motion-centric
information. Through our experiments, we show that our model outperforms
existing methods on two separate datasets, Ego4D and EpicKitchens-100 ("Unseen
Set"), as measured by several additional metrics, such as time to contact, and
next-active-object localization. The code will be available upon acceptance.
