37 citations · 64 across the 16 of their papers we have counts for
5 papers · 1 filter
Unearthing Skill-Level Insights for Understanding Trade-Offs of Foundation Models
Mazda Moayeri, Vidhisha Balachandran, Varun Chandrasekaran +6
With models getting stronger, evaluations have grown more complex, testing multiple skills in one benchmark and even in the same instance at once. However, skill-wise performance i…
Improving Compositional Attribute Binding in Text-to-Image Generative Models via Enhanced Text Embeddings
Arman Zarei, Keivan Rezaei, Samyadeep Basu +4
Text-to-image diffusion-based generative models have the stunning ability to generate photo-realistic images and achieve state-of-the-art low FID scores on challenging image genera…
An Introduction to Vision-Language Modeling
Florian Bordes, Richard Yuanzhe Pang, Anurag Ajay +38
Following the recent popularity of Large Language Models (LLMs), several attempts have been made to extend them to the visual domain. From having a visual assistant that could guid…
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…
Rethinking Artistic Copyright Infringements in the Era of Text-to-Image Generative Models
Mazda Moayeri, Samyadeep Basu, Sriram Balasubramanian +4
Recent text-to-image generative models such as Stable Diffusion are extremely adept at mimicking and generating copyrighted content, raising concerns amongst artists that their uni…