Five experimentations in computer vision: seeing (through) images from Large Scale Vision Datasets

Abstract

Using images from large-scale vision datasets (LSVDs), five practice-based studies – experimentations – were carried out to shed light on the visual content, replications of historical continuities, and precarious human labour behind computer vision. First, I focus my analysis on the dominant ideologies coming from a colonial mindset and modern taxonomy present in the visual content of the images. Then, in an exchange with microworkers, I highlight the decontextualized practices that these images undergo during their tagging and/or description, so that they become data for machine learning. Finally, using as reference two counterhegemonic initiatives from Latin America in the 1960s, I present a pedagogical experience constituting a dataset for computer vision based on works of art at a historical museum. The results offered by these experimentations serve to help speculate on more radical ways of seeing the world through machines.