You Have To Look More Than Once: Active and Continuous Exploration using YOLO

CVPR Workshop on Continuous and Open-Set Learning

Authors: Clemens-Alexander Brust and Christoph Käding and Joachim Denzler

Abstract: Traditionally, most research in the area of object detection builds on models trained once on reliable labeled data for a predefined application. However, in many application scenarios, new data becomes available over time or the distribution underlying the problem changes itself. In this case, models are usually retrained from scratch or refined via fine-tuning or incremental learning. For most applications, acquiring new labels is the limiting factor in terms of effort or costs. Active learning aims to minimize the labeling effort by selecting only valuable samples for annotation. It is widely studied in classification tasks, where different measures of uncertainty are the most common choice for selection. We combine the deep object detector YOLO with active learning and an incremental learning scheme to build an object detection system suitable for active and continuous exploration and open-set problems by querying whole images for annotation rather than single proposals.