  We present a scalable approach for Detecting Objects by transferring
Common-sense Knowledge (DOCK) from source to target categories. In our setting,
the training data for the source categories have bounding box annotations,
while those for the target categories only have image-level annotations.
Current state-of-the-art approaches focus on image-level visual or semantic
similarity to adapt a detector trained on the source categories to the new
target categories. In contrast, our key idea is to (i) use similarity not at
the image-level, but rather at the region-level, and (ii) leverage richer
common-sense (based on attribute, spatial, etc.) to guide the algorithm towards
learning the correct detections. We acquire such common-sense cues
automatically from readily-available knowledge bases without any extra human
effort. On the challenging MS COCO dataset, we find that common-sense knowledge
can substantially improve detection performance over existing transfer-learning
baselines.
