Computer Vision at the Mirror Stage: Questioning and Refining Visual Categorization (Talk)

Computer vision advancements in predicting and visualizing labels, often motivate us to consider the relationship between labels and images as a given. Yet, the prototypical nature of coherent labels, such as the alphabet of handwritten characters, can help us question assumed families of handwritten variation. At the same time conceptual categories such as the name of a country, if properly assigned to images, can provide a useful benchmark for state of the art computer vision models. Further, using synthesis methods these datasets can be mined to reveal patterns of hidden visual vocabularies that help improve our (geographical) data understanding. The goal of this talk is to motivate rethinking labels in a bidirectional way, aiming to create systems that inform how humans discretize their visual world.