This Massachusetts Institute of Technology (MIT) recent paper shows a more than interesting approach towards how machines can get to understand and interpret the relationships between objects in a scene. As this and other recent studies reflect, the pain is latent… deep learning models are getting very good at identifying objects in all kinds of scenes, however they can't understand the relationships of those objects with each other and the surrounding environment. Even simple relationships that are obvious for a human, like this is inside of this or that is on top of that, are very hard for widely used object detection and segmentation models. There is a growing number of use cases that will require this understanding. This evolution in the models will require new data for training.