  Object detection requires substantial labeling effort for learning robust
models. Active learning can reduce this effort by intelligently selecting
relevant examples to be annotated. However, selecting these examples properly
without introducing a sampling bias with a negative impact on the
generalization performance is not straightforward and most active learning
techniques can not hold their promises on real-world benchmarks. In our
evaluation paper, we focus on active learning techniques without a
computational overhead besides inference, something we refer to as zero-cost
active learning. In particular, we show that a key ingredient is not only the
score on a bounding box level but also the technique used for aggregating the
scores for ranking images. We outline our experimental setup and also discuss
practical considerations when using active learning for object detection.
