Before revision:  We apply our models to two settings: (1) species detection using camera trap data, which is sampled at a low, variable frame rate based on a motion trigger and used to study biodiversity, and (2) vehicle detection in traffic cameras, which have similarly low frame rate. We show that our model leads to performance gains over strong baselines in all settings .
After revision:  We apply our models to two settings: (1) species detection using camera trap data, which is sampled at a low, variable frame rate based on a motion trigger and used to study biodiversity, and (2) vehicle detection in traffic cameras, showing in both settings that Context R-CNN leads to performance gains over strong baselines in all settings .