  Unsupervised domain adaptation for object detection is a challenging problem
with many real-world applications. Unfortunately, it has received much less
attention than supervised object detection. Models that try to address this
task tend to suffer from a shortage of annotated training samples. Moreover,
existing methods of feature alignments are not sufficient to learn
domain-invariant representations. To address these limitations, we propose a
novel augmented feature alignment network (AFAN) which integrates intermediate
domain image generation and domain-adversarial training into a unified
framework. An intermediate domain image generator is proposed to enhance
feature alignments by domain-adversarial training with automatically generated
soft domain labels. The synthetic intermediate domain images progressively
bridge the domain divergence and augment the annotated source domain training
data. A feature pyramid alignment is designed and the corresponding feature
discriminator is used to align multi-scale convolutional features of different
semantic levels. Last but not least, we introduce a region feature alignment
and an instance discriminator to learn domain-invariant features for object
proposals. Our approach significantly outperforms the state-of-the-art methods
on standard benchmarks for both similar and dissimilar domain adaptations.
Further extensive experiments verify the effectiveness of each component and
demonstrate that the proposed network can learn domain-invariant
representations.
