  Multimedia applications are often associated with cross-domain knowledge
transfer, where Unsupervised Domain Adaptation (UDA) can be used to reduce the
domain shifts. Open Set Domain Adaptation (OSDA) aims to transfer knowledge
from a well-labeled source domain to an unlabeled target domain under the
assumption that the target domain contains unknown classes. Existing OSDA
methods consistently lay stress on the covariate shift, ignoring the potential
label shift problem. The performance of OSDA methods degrades drastically under
intra-domain class imbalance and inter-domain label shift. However, little
attention has been paid to this issue in the community. In this paper, the
Imbalanced Open Set Domain Adaptation (IOSDA) is explored where the covariate
shift, label shift and category mismatch exist simultaneously. To alleviate the
negative effects raised by label shift in OSDA, we propose Open-set
Moving-threshold Estimation and Gradual Alignment (OMEGA) - a novel
architecture that improves existing OSDA methods on class-imbalanced data.
Specifically, a novel unknown-aware target clustering scheme is proposed to
form tight clusters in the target domain to reduce the negative effects of
label shift and intra-domain class imbalance. Furthermore, moving-threshold
estimation is designed to generate specific thresholds for each target sample
rather than using one for all. Extensive experiments on IOSDA, OSDA and OPDA
benchmarks demonstrate that our method could significantly outperform existing
state-of-the-arts. Code and data are available at
https://github.com/mendicant04/OMEGA.
