Scene graph aims to faithfully reveal humans' perception of image content.
When humans analyze a scene, they usually prefer to describe image gist first,
namely major objects and key relations in a scene graph. This humans' inherent
perceptive habit implies that there exists a hierarchical structure about
humans' preference during the scene parsing procedure. Therefore, we argue that
a desirable scene graph should be also hierarchically constructed, and
introduce a new scheme for modeling scene graph. Concretely, a scene is
represented by a human-mimetic Hierarchical Entity Tree (HET) consisting of a
series of image regions. To generate a scene graph based on HET, we parse HET
with a Hybrid Long Short-Term Memory (Hybrid-LSTM) which specifically encodes
hierarchy and siblings context to capture the structured information embedded
in HET. To further prioritize key relations in the scene graph, we devise a
Relation Ranking Module (RRM) to dynamically adjust their rankings by learning
to capture humans' subjective perceptive habits from objective entity saliency
and size. Experiments indicate that our method not only achieves
state-of-the-art performances for scene graph generation, but also is expert in
mining image-specific relations which play a great role in serving downstream
tasks.

介绍了一种新的场景图生成方法，该方法利用人类对图像给出主要对象和关键关系的认知习惯构建一个基于层次结构的人类模拟分层实体树，利用 Hybrid-LSTM 对树进行解析来生成场景图，并且设计了一个关系排序模块，动态调整场景图中的关键关系的排序来提升图像的下游任务表现。