The setback of YOLO architectures is that they use predefined object categories that are a part of the training process. If you want to adapt YOLO to a new domain you need to retrain it with your new category label.

This work presents a version of YOLO that can work on new categories without needing to retrain the algorithm, but instead having a real-time "dictionary" of examples that you can seemlessly update. Seems like a very useful algorithm to me.

Edit: apologies i misread your comment I thought it was asking why this is different that regular YOLO

It was new to me, serf. And judging by the number of upvotes, it was new to a few other people too.