  Active learning selects informative samples for annotation within budget,
which has proven efficient recently on object detection. However, the widely
used active detection benchmarks conduct image-level evaluation, which is
unrealistic in human workload estimation and biased towards crowded images.
Furthermore, existing methods still perform image-level annotation, but equally
scoring all targets within the same image incurs waste of budget and redundant
labels. Having revealed above problems and limitations, we introduce a
box-level active detection framework that controls a box-based budget per
cycle, prioritizes informative targets and avoids redundancy for fair
comparison and efficient application.
  Under the proposed box-level setting, we devise a novel pipeline, namely
Complementary Pseudo Active Strategy (ComPAS). It exploits both human
annotations and the model intelligence in a complementary fashion: an efficient
input-end committee queries labels for informative objects only; meantime
well-learned targets are identified by the model and compensated with
pseudo-labels. ComPAS consistently outperforms 10 competitors under 4 settings
in a unified codebase. With supervision from labeled data only, it achieves
100% supervised performance of VOC0712 with merely 19% box annotations. On the
COCO dataset, it yields up to 4.3% mAP improvement over the second-best method.
ComPAS also supports training with the unlabeled pool, where it surpasses 90%
COCO supervised performance with 85% label reduction. Our source code is
publicly available at https://github.com/lyumengyao/blad.
