Logical Bias Learning for Object Relation Prediction
Xinyu Zhou Zihan Ji Anna Zhu
Scene graph generation (SGG) aims to automatically map an image into a
semantic structural graph for better scene understanding. It has attracted
significant attention for its ability to provide object and relation
information, enabling graph reasoning for downstream tasks. However, it faces
severe limitations in practice due to the biased data and training method. In
this paper, we present a more rational and effective strategy based on causal
inference for object relation prediction. To further evaluate the superiority
of our strategy, we propose an object enhancement module to conduct ablation
studies. Experimental results on the Visual Gnome 150 (VG-150) dataset
demonstrate the effectiveness of our proposed method. These contributions can
provide great potential for foundation models for decision-making.