The scene graph generation (SGG) task is designed to identify the predicates
based on the subject-object pairs.However,existing datasets generally include
two imbalance cases: one is the class imbalance from the predicted predicates
and another is the context imbalance from the given subject-object pairs, which
presents significant challenges for SGG. Most existing methods focus on the
imbalance of the predicted predicate while ignoring the imbalance of the
subject-object pairs, which could not achieve satisfactory results. To address
the two imbalance cases, we propose a novel Environment Invariant Curriculum
Relation learning (EICR) method, which can be applied in a plug-and-play
fashion to existing SGG methods. Concretely, to remove the imbalance of the
subject-object pairs, we first construct different distribution environments
for the subject-object pairs and learn a model invariant to the environment
changes. Then, we construct a class-balanced curriculum learning strategy to
balance the different environments to remove the predicate imbalance.
Comprehensive experiments conducted on VG and GQA datasets demonstrate that our
EICR framework can be taken as a general strategy for various SGG models, and
achieve significant improvements.

通过构建不同分布环境来平衡主 - 客体对的不平衡，并采用课程学习策略平衡不同环境，我们提出了一种新颖的环境不变课程关系学习方法，可应用于现有的场景图生成方法，实验证明该方法实现了重要的改进。