Scene Understanding: Do we even need Labels and Data ?
Raoul de Charette · 2023
Despite their ever growing sizes, computer vision datasets are doomed to reflect only a tiny fraction of our world. The induced biases raise ethical issues and show that blind reliance on data can have critical outcomes when it comes to applications like autonomous driving. In this talk, we will investigate visual scene understanding in the era of large-scale datasets, self-supervised learning and LLMs. Following our recently published research we will question our use of machine learning and data for real and open world scene understanding by navigating through these questions: Are we making the best use of existing datasets ? Can we benefit from more knowledge priors ? Can vision algorithms perform in the unknown open-world?