  Real-world scenarios often require the anticipation of object interactions in
unknown future, which would assist the decision-making process of both humans
and agents. To meet this challenge, we present a new task named Visual
Relationship Forecasting (VRF) in videos to explore the prediction of visual
relationships in a reasoning manner. Specifically, given a subject-object pair
with H existing frames, VRF aims to predict their future interactions for the
next T frames without visual evidence. To evaluate the VRF task, we introduce
two video datasets named VRF-AG and VRF-VidOR, with a series of
spatio-temporally localized visual relation annotations in a video. These two
datasets densely annotate 13 and 35 visual relationships in 1923 and 13447
video clips, respectively. In addition, we present a novel Graph Convolutional
Transformer (GCT) framework, which captures both object-level and frame-level
dependencies by spatio-temporal Graph Convolution Network and Transformer.
Experimental results on both VRF-AG and VRF-VidOR datasets demonstrate that GCT
outperforms the state-of-the-art sequence modelling methods on visual
relationship forecasting.

Computer Vision and Pattern Recognition;Artificial Intelligence