Knowledge-Embedded Routing Network for Scene Graph Generation
To understand a scene in depth not only involves locating/recognizing individual objects, but also requires to infer the relationships and interactions among them. However, since the distribution of real-world relationships is seriously unbalanced, existing methods perform quite poorly for the less...
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Zusammenfassung: | To understand a scene in depth not only involves locating/recognizing
individual objects, but also requires to infer the relationships and
interactions among them. However, since the distribution of real-world
relationships is seriously unbalanced, existing methods perform quite poorly
for the less frequent relationships. In this work, we find that the statistical
correlations between object pairs and their relationships can effectively
regularize semantic space and make prediction less ambiguous, and thus well
address the unbalanced distribution issue. To achieve this, we incorporate
these statistical correlations into deep neural networks to facilitate scene
graph generation by developing a Knowledge-Embedded Routing Network. More
specifically, we show that the statistical correlations between objects
appearing in images and their relationships, can be explicitly represented by a
structured knowledge graph, and a routing mechanism is learned to propagate
messages through the graph to explore their interactions. Extensive experiments
on the large-scale Visual Genome dataset demonstrate the superiority of the
proposed method over current state-of-the-art competitors. |
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DOI: | 10.48550/arxiv.1903.03326 |