  This work focuses on training dataset enhancement of informative relational
triplets for Scene Graph Generation (SGG). Due to the lack of effective
supervision, the current SGG model predictions perform poorly for informative
relational triplets with inadequate training samples. Therefore, we propose two
novel training dataset enhancement modules: Feature Space Triplet Augmentation
(FSTA) and Soft Transfer. FSTA leverages a feature generator trained to
generate representations of an object in relational triplets. The biased
prediction based sampling in FSTA efficiently augments artificial triplets
focusing on the challenging ones. In addition, we introduce Soft Transfer,
which assigns soft predicate labels to general relational triplets to make more
supervisions for informative predicate classes effectively. Experimental
results show that integrating FSTA and Soft Transfer achieve high levels of
both Recall and mean Recall in Visual Genome dataset. The mean of Recall and
mean Recall is the highest among all the existing model-agnostic methods.
