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 doma

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