no code implementations • CVPR 2024 • Pratyusha Sharma, Tamar Rott Shaham, Manel Baradad, Stephanie Fu, Adrian Rodriguez-Munoz, Shivam Duggal, Phillip Isola, Antonio Torralba
Although LLM-generated images do not look like natural images, results on image generation and the ability of models to correct these generated images indicate that precise modeling of strings can teach language models about numerous aspects of the visual world.
no code implementations • 8 Jun 2023 • Manel Baradad, Yuanzhen Li, Forrester Cole, Michael Rubinstein, Antonio Torralba, William T. Freeman, Varun Jampani
To infer object depth on a real image, we place the segmented object into the learned background prompt and run off-the-shelf depth networks.
no code implementations • 25 Mar 2023 • Rickard Brüel-Gabrielsson, Tongzhou Wang, Manel Baradad, Justin Solomon
We introduce Deep Augmentation, an approach to implicit data augmentation using dropout or PCA to transform a targeted layer within a neural network to improve performance and generalization.
1 code implementation • 29 Nov 2022 • Manel Baradad, Chun-Fu Chen, Jonas Wulff, Tongzhou Wang, Rogerio Feris, Antonio Torralba, Phillip Isola
Learning image representations using synthetic data allows training neural networks without some of the concerns associated with real images, such as privacy and bias.
1 code implementation • NeurIPS 2021 • Manel Baradad, Jonas Wulff, Tongzhou Wang, Phillip Isola, Antonio Torralba
We investigate a suite of image generation models that produce images from simple random processes.
1 code implementation • CVPR 2020 • Manel Baradad, Antonio Torralba
To account for this, we propose a system that directly regresses 3D world coordinates for each pixel.
1 code implementation • CVPR 2018 • Manel Baradad, Vickie Ye, Adam B. Yedidia, Frédo Durand, William T. Freeman, Gregory W. Wornell, Antonio Torralba
We present a method for inferring a 4D light field of a hidden scene from 2D shadows cast by a known occluder on a diffuse wall.
1 code implementation • 2 Dec 2017 • Amaia Salvador, Miriam Bellver, Victor Campos, Manel Baradad, Ferran Marques, Jordi Torres, Xavier Giro-i-Nieto
We present a recurrent model for semantic instance segmentation that sequentially generates binary masks and their associated class probabilities for every object in an image.