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.
翻译:对物体探测的不受监督的域适应是许多现实世界应用中一个具有挑战性的问题。 不幸的是,它比监督对象探测得到的关注少得多。 试图完成这项任务的模型往往缺乏附加说明的培训样本。 此外,现有的特征校正方法不足以学习域-异性表示法。 为了解决这些局限性,我们提议建立一个新型的增强特性校正网络(AFAN),将中间域图像生成和对域对称培训纳入一个统一的框架。 提议建立一个中间域图象生成器,用自动生成的软域名标签加强域对域对域培训的特征调整。 合成中间域图象逐渐缩小了域差异,并增加了附加说明的来源域域域域培训数据。 设计了一个特征金字塔校正,并使用相应的特征区分器来协调不同语系层次的多尺度共进化特征。 最后但并非最不重要的一点是,我们引入了一个区域特征校正和实例歧视器,以学习对象提案的域内变异性特征。我们的方法大大超越了在类似和不同域适应方面标准基准方面采用的方法。 进一步的广泛实验核查了每个域域图象,可以学习每个域图象。