  Humans reason with concepts and metaconcepts: we recognize red and green from
visual input; we also understand that they describe the same property of
objects (i.e., the color). In this paper, we propose the visual
concept-metaconcept learner (VCML) for joint learning of concepts and
metaconcepts from images and associated question-answer pairs. The key is to
exploit the bidirectional connection between visual concepts and metaconcepts.
Visual representations provide grounding cues for predicting relations between
unseen pairs of concepts. Knowing that red and green describe the same property
of objects, we generalize to the fact that cube and sphere also describe the
same property of objects, since they both categorize the shape of objects.
Meanwhile, knowledge about metaconcepts empowers visual concept learning from
limited, noisy, and even biased data. From just a few examples of purple cubes
we can understand a new color purple, which resembles the hue of the cubes
instead of the shape of them. Evaluation on both synthetic and real-world
datasets validates our claims.
