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Modeling Conceptual Understanding in Image Reference Games
Rodolfo Corona Rodriguez · Stephan Alaniz · Zeynep Akata

Thu Dec 12 10:45 AM -- 12:45 PM (PST) @ East Exhibition Hall B + C #79

An agent who interacts with a wide population of other agents needs to be aware that there may be variations in their understanding of the world. Furthermore, the machinery which they use to perceive may be inherently different, as is the case between humans and machines. In this work, we present both an image reference game between a speaker and a population of listeners where reasoning about the concepts other agents can comprehend is necessary and a model formulation with this capability. We focus on reasoning about the conceptual understanding of others, as well as adapting to novel gameplay partners and dealing with differences in perceptual machinery. Our experiments on three benchmark image/attribute datasets suggest that our learner indeed encodes information directly pertaining to the understanding of other agents, and that leveraging this information is crucial for maximizing gameplay performance.

Author Information

Rodolfo Corona Rodriguez (UC Berkeley)
Stephan Alaniz (Max Planck Institute for Informatics)
Zeynep Akata (University of Amsterdam)

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