  How to select relevant key objects and reason about the complex relationships
cross vision and linguistic domain are two key issues in many multi-modality
applications such as visual question answering (VQA). In this work, we
incorporate the visual commonsense information and propose a heterogeneous
graph contrastive learning method to better finish the visual reasoning task.
Our method is designed as a plug-and-play way, so that it can be quickly and
easily combined with a wide range of representative methods. Specifically, our
model contains two key components: the Commonsense-based Contrastive Learning
and the Graph Relation Network. Using contrastive learning, we guide the model
concentrate more on discriminative objects and relevant visual commonsense
attributes. Besides, thanks to the introduction of the Graph Relation Network,
the model reasons about the correlations between homogeneous edges and the
similarities between heterogeneous edges, which makes information transmission
more effective. Extensive experiments on four benchmarks show that our method
greatly improves seven representative VQA models, demonstrating its
effectiveness and generalizability.
