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Visual relations form the basis of understanding our compositional world, asrelationships between visual objects capture key information in a scene. It isthen advantageous to learn relations automatically from the data, as learningwith predefined labels cannot capture all possible relations. However, currentrelation learning methods typically require supervision, and are not designedto generalize to scenes with more complicated relational structures than thoseseen during training. Here, we introduce ViRel, a method for unsuperviseddiscovery and learning of Visual Relations with graph-level analogy. In asetting where scenes within a task share the same underlying relationalsubgraph structure, our learning method of contrasting isomorphic andnon-isomorphic graphs discovers the relations across tasks in an unsupervisedmanner. Once the relations are learned, ViRel can then retrieve the sharedrelational graph structure for each task by parsing the predicted relationalstructure. Using a dataset based on grid-world and the Abstract ReasoningCorpus, we show that our method achieves above 95% accuracy in relationclassification, discovers the relation graph structure for most tasks, andfurther generalizes to unseen tasks with more complicated relationalstructures.

ViRel: Unsupervised Visual Relations Discovery with Graph-level Analogy