2 citations · 5 across the 4 of their papers we have counts for
4 papers
Embracing Diversity: Interpretable Zero-shot classification beyond one vector per class
Mazda Moayeri, Michael Rabbat, Mark Ibrahim +1
Vision-language models enable open-world classification of objects without the need for any retraining. While this zero-shot paradigm marks a significant advance, even today's best…
Does Progress On Object Recognition Benchmarks Improve Real-World Generalization?
Megan Richards, Polina Kirichenko, Diane Bouchacourt +1
For more than a decade, researchers have measured progress in object recognition on ImageNet-based generalization benchmarks such as ImageNet-A, -C, and -R. Recent advances in foun…
Pinpointing Why Object Recognition Performance Degrades Across Income Levels and Geographies
Laura Gustafson, Megan Richards, Melissa Hall +3
Despite impressive advances in object-recognition, deep learning systems' performance degrades significantly across geographies and lower income levels raising pressing concerns of…
Measuring and signing fairness as performance under multiple stakeholder distributions
David Lopez-Paz, Diane Bouchacourt, Levent Sagun +1
As learning machines increase their influence on decisions concerning human lives, analyzing their fairness properties becomes a subject of central importance. Yet, our best tools…