  Large amounts of labeled training data are one of the main contributors to
the great success that deep models have achieved in the past. Label acquisition
for tasks other than benchmarks can pose a challenge due to requirements of
both funding and expertise. By selecting unlabeled examples that are promising
in terms of model improvement and only asking for respective labels, active
learning can increase the efficiency of the labeling process in terms of time
and cost.
  In this work, we describe combinations of an incremental learning scheme and
methods of active learning. These allow for continuous exploration of newly
observed unlabeled data. We describe selection criteria based on model
uncertainty as well as expected model output change (EMOC). An object detection
task is evaluated in a continuous exploration context on the PASCAL VOC
dataset. We also validate a weakly supervised system based on active and
incremental learning in a real-world biodiversity application where images from
camera traps are analyzed. Labeling only 32 images by accepting or rejecting
proposals generated by our method yields an increase in accuracy from 25.4% to
42.6%.
