We introduce a novel unsupervised domain adaptation approach for object detection. We aim to alleviate the imperfect translation problem of pixel-level adaptations, and the source-biased discriminativity problem of feature-level adaptations simultaneously. Our approach is composed of two stages, i.e., Domain Diversification (DD) and Multidomain-invariant Representation Learning (MRL). At the DD stage, we diversify the distribution of the labeled data by generating various distinctive shifted domains from the source domain. At the MRL stage, we apply adversarial learning with a multi-domain discriminator to encourage feature to be indistinguishable among the domains. DD addresses the source-biased discriminativity, while MRL mitigates the imperfect image translation. We construct a structured domain adaptation framework for our learning paradigm and introduce a practical way of DD for implementation. Our method outperforms the state-of-the-art methods by a large margin of 3% ∼ 12% in terms of mean average precision (mAP) on various datasets.
For pixel-level adaptation, the work REF devises a generative network to increase the diversity of the source domain, which is similar to data augmentation.
Diversify and Match: A Domain Adaptive Representation Learning Paradigm for Object Detection
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Computer Science