Carpe Diem: A Lifelong Learning Tool for Automated Wildlife Surveillance

Abstract

We introduce Carpe Diem, an interactive tool for object detection tasks such as automated wildlife surveillance. It reduces the annotation effort by a utomatically selecting informative images for annotation, facilitates the annotation process by proposing likely objects and labels, and accelerates the integration of new labels into the deep neural network model by avoiding re-training from scratch. Carpe Diem implements active learning, which intelligently explores unlabeled data and only selects valuable examples to avoid redundant annotations. This strategy saves expensive human resources. Moreover, incremental learning enables a continually improving model. Whenever new annotations are available, the model can be updated efficiently and quickly, without re-training, and regardless of the amount of accumulated training data. Because there is no single large training step, the model can be used to make predictions at any time. We exploit this in our annotation process, where users only confirm or reject proposals instead of manually drawing bounding boxes.