Learning and generalization of compositional representations of visual scenes

Understand

Complex visual scenes that are composed of multiple objects, each with attributes, such as object name, location, pose, color, etc., are challenging to describe in order to train neural networks.

• Usually,deep learning networks are trained supervised by categorical scene descriptions.
• The common categorical description of a scene contains the names of individual objects but lacks information about other attributes.
• Here, we use distributed representations of object attributes and vector operations in a vector symbolic architecture to create a full compositional description of a scene in a high-dimensional vector.