no code implementations • 29 Nov 2023 • Krish Kabra, Kathleen M. Lewis, Guha Balakrishnan
Results on real datasets show that GELDA can generate accurate and diverse visual attribute suggestions, and uncover biases such as confounding between class labels and background features.
1 code implementation • 21 Jul 2023 • Kathleen M. Lewis, Emily Mu, Adrian V. Dalca, John Guttag
We demonstrate the utility of GIST by fine-tuning vision-language models on the image-and-generated-text pairs to learn an aligned vision-language representation space for improved classification.
Fine-Grained Image Classification Image-text Classification +4
no code implementations • 5 Nov 2022 • Kathleen M. Lewis, John Guttag
Online clothing catalogs lack diversity in body shape and garment size.
no code implementations • 17 Feb 2021 • Harini Suresh, Kathleen M. Lewis, John V. Guttag, Arvind Satyanarayan
Interpretability methods aim to help users build trust in and understand the capabilities of machine learning models.
1 code implementation • CVPR 2020 • Amy Zhao, Guha Balakrishnan, Kathleen M. Lewis, Frédo Durand, John V. Guttag, Adrian V. Dalca
We present a probabilistic model that, given a single image of a completed painting, recurrently synthesizes steps of the painting process.
no code implementations • 17 Dec 2018 • Kathleen M. Lewis, Natalia S. Rost, John Guttag, Adrian V. Dalca
We present a learning-based registration method, SparseVM, that is more accurate and orders of magnitude faster than the most accurate clinical registration methods.