Visual relations form the basis of understanding our compositional world, as
relationships between visual objects capture key information in a scene. It is
then advantageous to learn relations automatically from the data, as learning
with predefined labels cannot capture all possible relations. However, current
relation learning methods typically require supervision, and are not designed
to generalize to scenes with more complicated relational structures than those
seen during training. Here, we introduce ViRel, a method for unsupervised
discovery and learning of Visual Relations with graph-level analogy. In a
setting where scenes within a task share the same underlying relational
subgraph structure, our learning method of contrasting isomorphic and
non-isomorphic graphs discovers the relations across tasks in an unsupervised
manner. Once the relations are learned, ViRel can then retrieve the shared
relational graph structure for each task by parsing the predicted relational
structure. Using a dataset based on grid-world and the Abstract Reasoning
Corpus, we show that our method achieves above 95% accuracy in relation
classification, discovers the relation graph structure for most tasks, and
further generalizes to unseen tasks with more complicated relational
structures.

本文提出了 ViRel，一种基于图级类比的无监督发现和学习视觉关系的方法。在一个共享着相同的底层关系子图结构的任务环境下，我们的学习方法通过对同构和非同构图形进行比较，以无监督的方式发现跨任务的关系。结果表明，我们的方法在关系分类方面达到了超过 95％的准确率，发现了大多数任务的关系图结构，并进一步推广到具有更复杂关系结构的未见过的任务上。