Existing Unbiased Scene Graph Generation (USGG) methods only focus on addressing the predicate-level imbalance that high-frequency classes dominate predictions of rare ones, while overlooking the concept-level imbalance. Actually, even if predicates themselves are balanced, there is still a significant concept-imbalance within them due to the long-tailed distribution of contexts (i.e., subject-object combinations). This concept-level imbalance poses a more pervasive and challenging issue compared to the predicate-level imbalance since subject-object pairs are inherently complex in combinations. Hence, we introduce a novel research problem: Generalized Unbiased Scene Graph Generation (G-USGG), which takes into account both predicate-level and concept-level imbalance. To the end, we propose the Multi-Concept Learning (MCL) framework, which ensures a balanced learning process across rare/ uncommon/ common concepts. MCL first quantifies the concept-level imbalance across predicates in terms of different amounts of concepts, representing as multiple concept-prototypes within the same class. It then effectively learns concept-prototypes by applying the Concept Regularization (CR) technique. Furthermore, to achieve balanced learning over different concepts, we introduce the Balanced Prototypical Memory (BPM), which guides SGG models to generate balanced representations for concept-prototypes. Extensive experiments demonstrate the remarkable efficacy of our model-agnostic strategy in enhancing the performance of benchmark models on both VG-SGG and OI-SGG datasets, leading to new state-of-the-art achievements in two key aspects: predicate-level unbiased relation recognition and concept-level compositional generability.

翻译：现有无偏场景图生成方法仅关注解决谓词层面的不平衡问题（即高频类别主导罕见类别的预测），而忽视了概念层面的不平衡。实际上，即使谓词本身达到平衡，由于上下文（即主-客体组合）呈现长尾分布，谓词内部仍存在显著的概念不平衡。与谓词层面不平衡相比，这种概念层面不平衡因主-客体对组合的固有复杂性而更具普遍性和挑战性。因此，我们提出全新研究问题：广义无偏场景图生成，同时考虑谓词层面和概念层面的不平衡。为此，我们提出多概念学习框架，确保对罕见/不常见/常见概念进行均衡学习。MCL首先通过同一类别内的多个概念原型量化不同谓词在概念数量上的概念不平衡性，继而应用概念正则化技术有效学习这些概念原型。此外，为实现不同概念的均衡学习，我们引入平衡原型记忆模块，引导SGG模型为概念原型生成平衡表征。大量实验表明，我们提出的与模型无关的策略在增强VG-SGG和OI-SGG数据集上基准模型性能方面具有显著效果，在谓词层面无偏关系识别与概念层面组合泛化性两个关键方面均实现了新的最佳性能。