  Active learning approaches in computer vision generally involve querying
strong labels for data. However, previous works have shown that weak
supervision can be effective in training models for vision tasks while greatly
reducing annotation costs. Using this knowledge, we propose an adaptive
supervision framework for active learning and demonstrate its effectiveness on
the task of object detection. Instead of directly querying bounding box
annotations (strong labels) for the most informative samples, we first query
weak labels and optimize the model. Using a switching condition, the required
supervision level can be increased. Our framework requires little to no change
in model architecture. Our extensive experiments show that the proposed
framework can be used to train good generalizable models with much lesser
annotation costs than the state of the art active learning approaches for
object detection.
