  Capturing the state changes of interacting objects is a key technology for
understanding human-object interactions. This technical report describes our
method using heterogeneous backbones for the Ego4D Object State Change
Classification and PNR Temporal Localization Challenge. In the challenge, we
used the heterogeneous video understanding backbones, namely CSN with 3D
convolution as operator and VideoMAE with Transformer as operator. Our method
achieves an accuracy of 0.796 on OSCC while achieving an absolute temporal
localization error of 0.516 on PNR. These excellent results rank 1st on the
leaderboard of Ego4D OSCC & PNR-TL Challenge 2022.
