  Unbiased Scene Graph Generation (USGG) aims to address biased predictions in
SGG. To that end, data transfer methods are designed to convert coarse-grained
predicates into fine-grained ones, mitigating imbalanced distribution. However,
them overlook contextual relevance between transferred labels and
subject-object pairs, such as unsuitability of 'eating' for 'woman-table'.
Furthermore, they typically involve a two-stage process with significant
computational costs, starting with pre-training a model for data transfer,
followed by training from scratch using transferred labels. Thus, we introduce
a plug-and-play method named CITrans, which iteratively trains SGG models with
progressively enhanced data. First, we introduce Context-Restricted Transfer
(CRT), which imposes subject-object constraints within predicates' semantic
space to achieve fine-grained data transfer. Subsequently, Efficient Iterative
Learning (EIL) iteratively trains models and progressively generates enhanced
labels which are consistent with model's learning state, thereby accelerating
the training process. Finally, extensive experiments show that CITrans achieves
state-of-the-art and results with high efficiency.

Computer Vision and Pattern Recognition;Artificial Intelligence