Region-level Active Detector Learning
This addresses the challenge of costly and inefficient labeling in real-world object detection, though it appears incremental as it builds on existing image-level and object-level methods.
The paper tackles the problem of active learning for object detection by introducing a region-level approach that reduces labeling effort and improves rare object search in imbalanced, cluttered scenes, showing significant decreases in labeling effort.
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.