The proposed compositional objective can be extended to other types of structured representations beyond object-centric models by adapting the composition strategy to suit the specific characteristics of the new representation types. For scene graphs, the compositional objective can focus on ensuring that the relationships between objects are accurately captured in the latent representations. This can involve constructing composite representations that not only combine object features but also incorporate the spatial and semantic relationships between objects in the scene. By maximizing the likelihood of the composite scene graph generated from the mixed object representations, the model can learn to encode compositional information about the scene structure.
Similarly, for relational reasoning tasks, the compositional objective can emphasize capturing the interactions and dependencies between entities in the scene. The composition strategy can involve mixing entity representations while preserving the relational information encoded in the latent space. By evaluating the validity of the composite scene based on relational constraints and dependencies, the model can learn to represent complex relational structures in a compositional manner.
In essence, extending the compositional objective to other structured representations involves tailoring the composition strategy to the specific characteristics and relationships inherent in the new representation types, ensuring that the model learns to encode and manipulate structured information effectively.