  Active learning for object detection is conventionally achieved by applying
techniques developed for classification in a way that aggregates individual
detections into image-level selection criteria. This is typically coupled with
the costly assumption that every image selected for labelling must be
exhaustively annotated. This yields incremental improvements on well-curated
vision datasets and struggles in the presence of data imbalance and visual
clutter that occurs in real-world imagery. Alternatives to the image-level
approach are surprisingly under-explored in the literature. In this work, we
introduce a new strategy that subsumes previous Image-level and Object-level
approaches into a generalized, Region-level approach that promotes
spatial-diversity by avoiding nearby redundant queries from the same image and
minimizes context-switching for the labeler. We show that this approach
significantly decreases labeling effort and improves rare object search on
realistic data with inherent class-imbalance and cluttered scenes.
