  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.
