  We present a first-person method for cooperative basketball intention
prediction: we predict with whom the camera wearer will cooperate in the near
future from unlabeled first-person images. This is a challenging task that
requires inferring the camera wearer's visual attention, and decoding the
social cues of other players. Our key observation is that a first-person view
provides strong cues to infer the camera wearer's momentary visual attention,
and his/her intentions. We exploit this observation by proposing a new
cross-model EgoSupervision learning scheme that allows us to predict with whom
the camera wearer will cooperate in the near future, without using manually
labeled intention labels. Our cross-model EgoSupervision operates by
transforming the outputs of a pretrained pose-estimation network, into pseudo
ground truth labels, which are then used as a supervisory signal to train a new
network for a cooperative intention task. We evaluate our method, and show that
it achieves similar or even better accuracy than the fully supervised methods
do.
