Bridging Non Co-occurrence with Unlabeled In-the-wild Data for
Incremental Object Detection
Bridging Non Co-occurrence with Unlabeled In-the-wild Data for
Incremental Object Detection
Deep networks have shown remarkable results in the task of object detection. However, their performance suffers critical drops when they are subsequently trained on novel classes without any sample from the base classes originally used to train the model. This phenomenon is known as catastrophic forgetting. Recently, several incremental learning …