  Active learning has been demonstrated effective to reduce labeling cost,
while most progress has been designed for image recognition, there still lacks
instance-level active learning for object detection. In this paper, we rethink
two key components, i.e., localization and recognition, for object detection,
and find that the correctness of them are highly related, therefore, it is not
necessary to annotate both boxes and classes if we are given pseudo annotations
provided with the trained model. Motivated by this, we propose an efficient
query strategy, termed as DeLR, that Decoupling the Localization and
Recognition for active query. In this way, we are probably free of class
annotations when the localization is correct, and able to assign the labeling
budget for more informative samples. There are two main differences in DeLR: 1)
Unlike previous methods mostly focus on image-level annotations, where the
queried samples are selected and exhausted annotated. In DeLR, the query is
based on region-level, and we only annotate the object region that is queried;
2) Instead of directly providing both localization and recognition annotations,
we separately query the two components, and thus reduce the recognition budget
with the pseudo class labels provided by the model. Experiments on several
benchmarks demonstrate its superiority. We hope our proposed query strategy
would shed light on researches in active learning in object detection.
