One stage relation detection (detects objects and relations simultaneously).

这个模型受启发于CenterNet，通过添加一个新的用于生成RAF的卷积头来获取物体之间的关系。

具体RAF是如何定义的，以及如何训练(Loss)：

Results

这篇看不懂。。。 Can effectively utilize structural information in the output space

Building an efficient structured representation that captures comprehensive semantic knowledge is a crucial step towards a deeper understanding of visual scenes

SGTR 是一种自上而下的方法，该方法首先使用基于Transformer的生成器来生成一组可学习的triplet queries (subject–predicate–object)，然后使用级联的triplet detector逐步完善这些查询并生成最终场景图。它还提出了一种基于结构化发生器的实体感知关系表示方法，该方法利用了关系的组成属性。

Top-down approach (SGTR):
- Starts with higher-level structures (triplet queries) and refines them
- Begins by generating complete subject-predicate-object triplet candidates
- Then progressively refines these triplets to match the image content
- Works with the complete structural units from the beginning
- Analogous to starting with a rough sketch of the entire tree and then refining each branch