no code implementations • CVPR 2016 • German Ros, Laura Sellart, Joanna Materzynska, David Vazquez, Antonio M. Lopez
In order to answer this question we have generated a synthetic collection of diverse urban images, named SYNTHIA, with automatically generated class annotations.
1 code implementation • CVPR 2020 • Joanna Materzynska, Tete Xiao, Roei Herzig, Huijuan Xu, Xiaolong Wang, Trevor Darrell
Human action is naturally compositional: humans can easily recognize and perform actions with objects that are different from those used in training demonstrations.
no code implementations • CVPR 2022 • Joanna Materzynska, Antonio Torralba, David Bau
The CLIP network measures the similarity between natural text and images; in this work, we investigate the entanglement of the representation of word images and natural images in its image encoder.
2 code implementations • ICCV 2023 • Rohit Gandikota, Joanna Materzynska, Jaden Fiotto-Kaufman, David Bau
We propose a fine-tuning method that can erase a visual concept from a pre-trained diffusion model, given only the name of the style and using negative guidance as a teacher.
1 code implementation • NeurIPS 2023 • Sarah Schwettmann, Tamar Rott Shaham, Joanna Materzynska, Neil Chowdhury, Shuang Li, Jacob Andreas, David Bau, Antonio Torralba
FIND contains functions that resemble components of trained neural networks, and accompanying descriptions of the kind we seek to generate.
1 code implementation • 20 Nov 2023 • Rohit Gandikota, Joanna Materzynska, Tingrui Zhou, Antonio Torralba, David Bau
We present a method to create interpretable concept sliders that enable precise control over attributes in image generations from diffusion models.
no code implementations • 7 Dec 2023 • Joanna Materzynska, Josef Sivic, Eli Shechtman, Antonio Torralba, Richard Zhang, Bryan Russell
To avoid overfitting to the new custom motion, we introduce an approach for regularization over videos.