  Human actions often involve complex interactions across several inter-related
objects in the scene. However, existing approaches to fine-grained video
understanding or visual relationship detection often rely on single object
representation or pairwise object relationships. Furthermore, learning
interactions across multiple objects in hundreds of frames for video is
computationally infeasible and performance may suffer since a large
combinatorial space has to be modeled. In this paper, we propose to efficiently
learn higher-order interactions between arbitrary subgroups of objects for
fine-grained video understanding. We demonstrate that modeling object
interactions significantly improves accuracy for both action recognition and
video captioning, while saving more than 3-times the computation over
traditional pairwise relationships. The proposed method is validated on two
large-scale datasets: Kinetics and ActivityNet Captions. Our SINet and
SINet-Caption achieve state-of-the-art performances on both datasets even
though the videos are sampled at a maximum of 1 FPS. To the best of our
knowledge, this is the first work modeling object interactions on open domain
large-scale video datasets, and we additionally model higher-order object
interactions which improves the performance with low computational costs.
