  Video scene graph generation (VidSGG) aims to parse the video content into
scene graphs, which involves modeling the spatio-temporal contextual
information in the video. However, due to the long-tailed training data in
datasets, the generalization performance of existing VidSGG models can be
affected by the spatio-temporal conditional bias problem. In this work, from
the perspective of meta-learning, we propose a novel Meta Video Scene Graph
Generation (MVSGG) framework to address such a bias problem. Specifically, to
handle various types of spatio-temporal conditional biases, our framework first
constructs a support set and a group of query sets from the training data,
where the data distribution of each query set is different from that of the
support set w.r.t. a type of conditional bias. Then, by performing a novel meta
training and testing process to optimize the model to obtain good testing
performance on these query sets after training on the support set, our
framework can effectively guide the model to learn to well generalize against
biases. Extensive experiments demonstrate the efficacy of our proposed
framework.
