  The quality and size of training sets often limit the performance of many
state of the art object detectors. However, in many scenarios, it can be
difficult to collect images for training, not to mention the costs associated
with collecting annotations suitable for training these object detectors. For
these reasons, on challenging video datasets such as the Dataset for Underwater
Substrate and Invertebrate Analysis (DUSIA), budgets may only allow for
collecting and providing partial annotations. To aid in the challenges
associated with training with limited and partial annotations, we introduce
Context Matched Collages, which leverage explicit context labels to combine
unused background examples with existing annotated data to synthesize
additional training samples that ultimately improve object detection
performance. By combining a set of our generated collage images with the
original training set, we see improved performance using three different object
detectors on DUSIA, ultimately achieving state of the art object detection
performance on the dataset.
