  Scene Graph Generation (SGG) remains a challenging task due to its
compositional property. Previous approaches improve prediction efficiency by
learning in an end-to-end manner. However, these methods exhibit limited
performance as they assume unidirectional conditioning between entities and
predicates, leading to insufficient information interaction. To address this
limitation, we propose a novel bidirectional conditioning factorization for
SGG, introducing efficient interaction between entities and predicates.
Specifically, we develop an end-to-end scene graph generation model,
Bidirectional Conditioning Transformer (BCTR), to implement our factorization.
BCTR consists of two key modules. First, the Bidirectional Conditioning
Generator (BCG) facilitates multi-stage interactive feature augmentation
between entities and predicates, enabling mutual benefits between the two
predictions. Second, Random Feature Alignment (RFA) regularizes the feature
space by distilling multi-modal knowledge from pre-trained models, enhancing
BCTR's ability on tailed categories without relying on statistical priors. We
conduct a series of experiments on Visual Genome and Open Image V6,
demonstrating that BCTR achieves state-of-the-art performance on both
benchmarks. The code will be available upon acceptance of the paper.
