Getting it Right: Improving Spatial Consistency in Text-to-Image Models

1 code implementation • 1 Apr 2024 • Agneet Chatterjee, Gabriela Ben Melech Stan, Estelle Aflalo, Sayak Paul, Dhruba Ghosh, Tejas Gokhale, Ludwig Schmidt, Hannaneh Hajishirzi, Vasudev Lal, Chitta Baral, Yezhou Yang

One of the key shortcomings in current text-to-image (T2I) models is their inability to consistently generate images which faithfully follow the spatial relationships specified in the text prompt.

GenEval: An Object-Focused Framework for Evaluating Text-to-Image Alignment

1 code implementation • NeurIPS 2023 • Dhruba Ghosh, Hanna Hajishirzi, Ludwig Schmidt

Recent breakthroughs in diffusion models, multimodal pretraining, and efficient finetuning have led to an explosion of text-to-image generative models.

Attribute Object +2

DataComp: In search of the next generation of multimodal datasets

1 code implementation • NeurIPS 2023 • Samir Yitzhak Gadre, Gabriel Ilharco, Alex Fang, Jonathan Hayase, Georgios Smyrnis, Thao Nguyen, Ryan Marten, Mitchell Wortsman, Dhruba Ghosh, Jieyu Zhang, Eyal Orgad, Rahim Entezari, Giannis Daras, Sarah Pratt, Vivek Ramanujan, Yonatan Bitton, Kalyani Marathe, Stephen Mussmann, Richard Vencu, Mehdi Cherti, Ranjay Krishna, Pang Wei Koh, Olga Saukh, Alexander Ratner, Shuran Song, Hannaneh Hajishirzi, Ali Farhadi, Romain Beaumont, Sewoong Oh, Alex Dimakis, Jenia Jitsev, Yair Carmon, Vaishaal Shankar, Ludwig Schmidt

Multimodal datasets are a critical component in recent breakthroughs such as Stable Diffusion and GPT-4, yet their design does not receive the same research attention as model architectures or training algorithms.

The Effect of Model Size on Worst-Group Generalization

no code implementations • 8 Dec 2021 • Alan Pham, Eunice Chan, Vikranth Srivatsa, Dhruba Ghosh, Yaoqing Yang, Yaodong Yu, Ruiqi Zhong, Joseph E. Gonzalez, Jacob Steinhardt

Overparameterization is shown to result in poor test accuracy on rare subgroups under a variety of settings where subgroup information is known.

Are Larger Pretrained Language Models Uniformly Better? Comparing Performance at the Instance Level

1 code implementation • Findings (ACL) 2021 • Ruiqi Zhong, Dhruba Ghosh, Dan Klein, Jacob Steinhardt

We develop statistically rigorous methods to address this, and after accounting for pretraining and finetuning noise, we find that our BERT-Large is worse than BERT-Mini on at least 1-4% of instances across MNLI, SST-2, and QQP, compared to the overall accuracy improvement of 2-10%.

QQP SST-2