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Semantic Image Interpretation is the task of extracting a structured semanticdescription from images. This requires the detection of visual relationships:triples (subject,relation,object) describing a semantic relation between asubject and an object. A pure supervised approach to visual relationshipdetection requires a complete and balanced training set for all the possiblecombinations of (subject, relation, object). However, such training sets arenot available and would require a prohibitive human effort. This implies theability of predicting triples which do not appear in the training set. Thisproblem is called zero-shot learning. State-of-the-art approaches to zero-shotlearning exploit similarities among relationships in the training set orexternal linguistic knowledge. In this paper, we perform zero-shot learning byusing Logic Tensor Networks, a novel Statistical Relational Learning frameworkthat exploits both the similarities with other seen relationships andbackground knowledge, expressed with logical constraints between subjects,relations and objects. The experiments on the Visual Relationship Dataset showthat the use of logical constraints outperforms the current methods. Thisimplies that background knowledge can be used to alleviate the incompletenessof training sets.

Compensating Supervision Incompleteness with Prior Knowledge in Semantic Image Interpretation