  Domain adaptation for object detection (DAOD) has recently drawn much
attention owing to its capability of detecting target objects without any
annotations. To tackle the problem, previous works focus on aligning features
extracted from partial levels (e.g., image-level, instance-level, RPN-level) in
a two-stage detector via adversarial training. However, individual levels in
the object detection pipeline are closely related to each other and this
inter-level relation is unconsidered yet. To this end, we introduce a novel
framework for DAOD with three proposed components: Multi-scale-aware
Uncertainty Attention (MUA), Transferable Region Proposal Network (TRPN), and
Dynamic Instance Sampling (DIS). With these modules, we seek to reduce the
negative transfer effect during training while maximizing transferability as
well as discriminability in both domains. Finally, our framework implicitly
learns domain invariant regions for object detection via exploiting the
transferable information and enhances the complementarity between different
detection levels by collaboratively utilizing their domain information. Through
ablation studies and experiments, we show that the proposed modules contribute
to the performance improvement in a synergic way, demonstrating the
effectiveness of our method. Moreover, our model achieves a new
state-of-the-art performance on various benchmarks.
