  Short-Term object-interaction Anticipation (STA) consists of detecting the
location of the next-active objects, the noun and verb categories of the
interaction, and the time to contact from the observation of egocentric video.
We propose STAformer, a novel attention-based architecture integrating
frame-guided temporal pooling, dual image-video attention, and multi-scale
feature fusion to support STA predictions from an image-input video pair.
Moreover, we introduce two novel modules to ground STA predictions on human
behavior by modeling affordances. First, we integrate an environment affordance
model which acts as a persistent memory of interactions that can take place in
a given physical scene. Second, we predict interaction hotspots from the
observation of hands and object trajectories, increasing confidence in STA
predictions localized around the hotspot. On the test set, our results obtain a
final 33.5 N mAP, 17.25 N+V mAP, 11.77 N+{\delta} mAP and 6.75 Overall top-5
mAP metric when trained on the v2 training dataset.
