Domain Adaptation for Fair and Robust Computer Vision

Abstract:

While recent progress significantly advances the state of the art in computer vision across several tasks, the poor ability of these models to generalize to domains and categories under-represented in the training set remains a problem, posing a direct challenge to fair and inclusive computer vision. In my talk, I will talk about my recent efforts towards improving generalizability and robustness in computer vision using domain adaptation. First, I will talk about our work on scaling domain adaptation to large scale datasets using metric learning. Next, I will introduce our new dataset effort called GeoNet aimed at benchmarking and developing novel algorithms towards geographical robustness in various vision tasks. Finally, I will talk about some research directions for the future in terms of leveraging rich multimodal (vision, language) data to improve adaptation of visual models to new domains.