  Open set domain recognition has got the attention in recent years. The task
aims to specifically classify each sample in the practical unlabeled target
domain, which consists of all known classes in the manually labeled source
domain and target-specific unknown categories. The absence of annotated
training data or auxiliary attribute information for unknown categories makes
this task especially difficult. Moreover, exiting domain discrepancy in label
space and data distribution further distracts the knowledge transferred from
known classes to unknown classes. To address these issues, this work presents
an end-to-end model based on attention-based GCN and semantic matching
optimization, which first employs the attention mechanism to enable the central
node to learn more discriminating representations from its neighbors in the
knowledge graph. Moreover, a coarse-to-fine semantic matching optimization
approach is proposed to progressively bridge the domain gap. Experimental
results validate that the proposed model not only has superiority on
recognizing the images of known and unknown classes, but also can adapt to
various openness of the target domain.
