  Prior highly-tuned image parsing models are usually studied in a certain
domain with a specific set of semantic labels and can hardly be adapted into
other scenarios (e.g., sharing discrepant label granularity) without extensive
re-training. Learning a single universal parsing model by unifying label
annotations from different domains or at various levels of granularity is a
crucial but rarely addressed topic. This poses many fundamental learning
challenges, e.g., discovering underlying semantic structures among different
label granularity or mining label correlation across relevant tasks. To address
these challenges, we propose a graph reasoning and transfer learning framework,
named "Graphonomy", which incorporates human knowledge and label taxonomy into
the intermediate graph representation learning beyond local convolutions. In
particular, Graphonomy learns the global and structured semantic coherency in
multiple domains via semantic-aware graph reasoning and transfer, enforcing the
mutual benefits of the parsing across domains (e.g., different datasets or
co-related tasks). The Graphonomy includes two iterated modules: Intra-Graph
Reasoning and Inter-Graph Transfer modules. The former extracts the semantic
graph in each domain to improve the feature representation learning by
propagating information with the graph; the latter exploits the dependencies
among the graphs from different domains for bidirectional knowledge transfer.
We apply Graphonomy to two relevant but different image understanding research
topics: human parsing and panoptic segmentation, and show Graphonomy can handle
both of them well via a standard pipeline against current state-of-the-art
approaches. Moreover, some extra benefit of our framework is demonstrated,
e.g., generating the human parsing at various levels of granularity by unifying
annotations across different datasets.
