Incomplete Multi-View Domain Adaptation via Channel Enhancement and Knowledge Transfer

Haifeng Xia, Pu Wang, Zhengming Ding ;

"Unsupervised domain adaptation (UDA) borrows well-labeled source knowledge to solve the specific task on unlabeled target domain with the assumption that both domains are from a single sensor, e.g., RGB or depth images. To boost model performance, multiple sensors are deployed on new-produced devices like autonomous vehicles to benefit from enriched information. However, the model trained with multi-view data difficultly becomes compatible with conventional devices only with a single sensor. This scenario is defined as incomplete multi-view domain adaptation (IMVDA), which considers that the source domain consists of multi-view data while the target domain only includes single-view instances. To overcome this practical demand, this paper proposes a novel Channel Enhancement and Knowledge Transfer (CEKT) framework with two modules. Concretely, the source channel enhancement module distinguishes view-common from view-specific channels and explores channel similarity to magnify the representation of important channels. Moreover, the adaptive knowledge transfer module attempts to enhance target representation towards multi-view semantic through implicit missing view recovery and adaptive cross-domain alignment. Extensive experimental results illustrate the effectiveness of our method in solving the IMVDA challenge."