Michal Drozdzal

research

∙ 05/15/2023

Improved baselines for vision-language pre-training

Contrastive learning has emerged as an efficient framework to learn mult...

0 Enrico Fini, et al. ∙

research

∙ 04/26/2023

Controllable Image Generation via Collage Representations

Recent advances in conditional generative image models have enabled impr...

0 Arantxa Casanova, et al. ∙

research

∙ 03/16/2023

Instance-Conditioned GAN Data Augmentation for Representation Learning

Data augmentation has become a crucial component to train state-of-the-a...

0 Pietro Astolfi, et al. ∙

research

∙ 02/15/2023

Learning to Substitute Ingredients in Recipes

Recipe personalization through ingredient substitution has the potential...

0 Bahare Fatemi, et al. ∙

research

∙ 11/03/2022

ImageNet-X: Understanding Model Mistakes with Factor of Variation Annotations

Deep learning vision systems are widely deployed across applications whe...

2 Badr Youbi Idrissi, et al. ∙

research

∙ 10/03/2022

Uncertainty-Driven Active Vision for Implicit Scene Reconstruction

Multi-view implicit scene reconstruction methods have become increasingl...

0 Edward J. Smith, et al. ∙

research

∙ 03/30/2022

On learning adaptive acquisition policies for undersampled multi-coil MRI reconstruction

Most current approaches to undersampled multi-coil MRI reconstruction fo...

0 Tim Bakker, et al. ∙

research

∙ 10/25/2021

Parameter Prediction for Unseen Deep Architectures

Deep learning has been successful in automating the design of features i...

0 Boris Knyazev, et al. ∙

research

∙ 09/10/2021

Instance-Conditioned GAN

Generative Adversarial Networks (GANs) can generate near photo realistic...

1 Arantxa Casanova, et al. ∙

research

∙ 07/20/2021

Active 3D Shape Reconstruction from Vision and Touch

Humans build 3D understandings of the world through active object explor...

6 Edward J. Smith, et al. ∙

research

∙ 12/07/2020

Generating unseen complex scenes: are we there yet?

Although recent complex scene conditional generation models generate inc...

0 Arantxa Casanova, et al. ∙

research

∙ 07/30/2020

Instance Selection for GANs

Recent advances in Generative Adversarial Networks (GANs) have led to th...

0 Terrance DeVries, et al. ∙

research

∙ 07/20/2020

Active MR k-space Sampling with Reinforcement Learning

Deep learning approaches have recently shown great promise in accelerati...

5 Luis Pineda, et al. ∙

research

∙ 07/07/2020

3D Shape Reconstruction from Vision and Touch

When a toddler is presented a new toy, their instinctual behaviour is to...

7 Edward J. Smith, et al. ∙

research

∙ 04/15/2020

Extending Unsupervised Neural Image Compression With Supervised Multitask Learning

We focus on the problem of training convolutional neural networks on gig...

0 David Tellez, et al. ∙

research

∙ 08/16/2019

Needles in Haystacks: On Classifying Tiny Objects in Large Images

In some computer vision domains, such as medical or hyperspectral imagin...

6 Nick Pawlowski, et al. ∙

research

∙ 07/11/2019

On the Evaluation of Conditional GANs

Conditional Generative Adversarial Networks (cGANs) are finding increasi...

8 Terrance DeVries, et al. ∙

research

∙ 04/11/2019

Elucidating image-to-set prediction: An analysis of models, losses and datasets

In recent years, we have experienced a flurry of contributions in the mu...

0 Luis Pineda, et al. ∙

research

∙ 02/08/2019

Reducing Uncertainty in Undersampled MRI Reconstruction with Active Acquisition

The goal of MRI reconstruction is to restore a high fidelity image from ...

0 Zizhao Zhang, et al. ∙

research

∙ 01/13/2019

The Liver Tumor Segmentation Benchmark (LiTS)

In this work, we report the set-up and results of the Liver Tumor Segmen...

8 Patrick Bilic, et al. ∙

research

∙ 12/14/2018

Inverse Cooking: Recipe Generation from Food Images

People enjoy food photography because they appreciate food. Behind each ...

0 Amaia Salvador, et al. ∙

research

∙ 11/21/2018

fastMRI: An Open Dataset and Benchmarks for Accelerated MRI

Accelerating Magnetic Resonance Imaging (MRI) by taking fewer measuremen...

21 Jure Žbontar, et al. ∙

research

∙ 04/30/2018

On the iterative refinement of densely connected representation levels for semantic segmentation

State-of-the-art semantic segmentation approaches increase the receptive...

0 Arantxa Casanova, et al. ∙

research

∙ 10/06/2017

Learnable Explicit Density for Continuous Latent Space and Variational Inference

In this paper, we study two aspects of the variational autoencoder (VAE)...

0 Chin-Wei Huang, et al. ∙

research

∙ 05/21/2017

Image Segmentation by Iterative Inference from Conditional Score Estimation

Inspired by the combination of feedforward and iterative computations in...

0 Adriana Romero, et al. ∙

research

∙ 12/02/2016

A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images

Colorectal cancer (CRC) is the third cause of cancer death worldwide. Cu...

0 David Vazquez, et al. ∙

research

∙ 11/28/2016

The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation

State-of-the-art approaches for semantic image segmentation are built on...

0 Simon Jégou, et al. ∙

research

∙ 08/14/2016

The Importance of Skip Connections in Biomedical Image Segmentation

In this paper, we study the influence of both long and short skip connec...

0 Michal Drozdzal, et al. ∙

research

∙ 07/26/2016

Generic Feature Learning for Wireless Capsule Endoscopy Analysis

The interpretation and analysis of the wireless capsule endoscopy record...

0 Santi Seguí, et al. ∙

Michal Drozdzal

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