  Scene graph generation refers to the task of automatically mapping an image
into a semantic structural graph, which requires correctly labeling each
extracted object and their interaction relationships. Despite the recent
success in object detection using deep learning techniques, inferring complex
contextual relationships and structured graph representations from visual data
remains a challenging topic. In this study, we propose a novel Attentive
Relational Network that consists of two key modules with an object detection
backbone to approach this problem. The first module is a semantic
transformation module utilized to capture semantic embedded relation features,
by translating visual features and linguistic features into a common semantic
space. The other module is a graph self-attention module introduced to embed a
joint graph representation through assigning various importance weights to
neighboring nodes. Finally, accurate scene graphs are produced by the relation
inference module to recognize all entities and the corresponding relations. We
evaluate our proposed method on the widely-adopted Visual Genome Dataset, and
the results demonstrate the effectiveness and superiority of our model.
