  In this paper, we propose a novel end-to-end unsupervised deep domain
adaptation model for adaptive object detection by exploiting multi-label object
recognition as a dual auxiliary task. The model exploits multi-label prediction
to reveal the object category information in each image and then uses the
prediction results to perform conditional adversarial global feature alignment,
such that the multi-modal structure of image features can be tackled to bridge
the domain divergence at the global feature level while preserving the
discriminability of the features. Moreover, we introduce a prediction
consistency regularization mechanism to assist object detection, which uses the
multi-label prediction results as an auxiliary regularization information to
ensure consistent object category discoveries between the object recognition
task and the object detection task. Experiments are conducted on a few
benchmark datasets and the results show the proposed model outperforms the
state-of-the-art comparison methods.
