Search Results for author: Manel Baradad

Found 8 papers, 5 papers with code

Recurrent Neural Networks for Semantic Instance Segmentation

1 code implementation2 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.

Instance Segmentation Object +2

Learning to See by Looking at Noise

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.

Image Generation

Procedural Image Programs for Representation Learning

1 code implementation29 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.

Representation Learning

Inferring Light Fields From Shadows

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.

Deep Augmentation: Self-Supervised Learning with Transformations in Activation Space

no code implementations25 Mar 2023 Rickard Brüel-Gabrielsson, Tongzhou Wang, Manel Baradad, Justin Solomon

We use this observation to formulate a method for selecting which layer to target; in particular, our experimentation reveals that targeting deeper layers with Deep Augmentation outperforms augmenting the input data.

Contrastive Learning Data Augmentation +2

Background Prompting for Improved Object Depth

no code implementations8 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.

Object

A Vision Check-up for Language Models

no code implementations3 Jan 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.

Image Generation Representation Learning

Cannot find the paper you are looking for? You can Submit a new open access paper.