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We Have So Much In Common: Modeling Semantic Relational Set Abstractions in Videos
[article]
2020
arXiv
pre-print
Identifying common patterns among events is a key ability in human and machine perception, as it underlies intelligent decision making. We propose an approach for learning semantic relational set abstractions on videos, inspired by human learning. We combine visual features with natural language supervision to generate high-level representations of similarities across a set of videos. This allows our model to perform cognitive tasks such as set abstraction (which general concept is in common
arXiv:2008.05596v1
fatcat:wte6tlsrsbazjiis3tqv3fj5rm