Decomposed Prototype Learning for Few-Shot Scene Graph Generation

Today's scene graph generation (SGG) models typically require abundant manual annotations to learn new predicate types. Thus, it is difficult to apply them to real-world applications with a long-tailed distribution of predicates. In this paper, we focus on a new promising task of SGG: few-shot SGG (FSSGG). FSSGG encourages models to be able to quickly transfer previous knowledge and recognize novel predicates well with only a few examples. Although many advanced approaches have achieved great success on few-shot learning (FSL) tasks, straightforwardly extending them into FSSGG is not applicable due to two intrinsic characteristics of predicate concepts: 1) Each predicate category commonly has multiple semantic meanings under different contexts. 2) The visual appearance of relation triplets with the same predicate differs greatly under different subject-object pairs. Both issues make it hard to model conventional latent representations for predicate categories with state-of-the-art FSL methods. To this end, we propose a novel Decomposed Prototype Learning (DPL). Specifically, we first construct a decomposable prototype space to capture intrinsic visual patterns of subjects and objects for predicates, and enhance their feature representations with these decomposed prototypes. Then, we devise an intelligent metric learner to assign adaptive weights to each support sample by considering the relevance of their subject-object pairs. We further re-split the VG dataset and compare DPL with various FSL methods to benchmark this task. Extensive results show that DPL achieves excellent performance in both base and novel categories.