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.

本文重点在于场景图生成（SGG）的训练数据集增强，通过引入 Feature Space Triplet Augmentation（FSTA）和 Soft Transfer 两个模块，成功提高了对具有挑战性的关系三元组的预测准确性，并在 Visual Genome 数据集中取得了较高的召回率。