Michal Drozdzal

Controllable Image Generation via Collage Representations

Arantxa Casanova

Marlene Careil

Adriana Romero

Christopher Pal

Jakob Verbeek

2023-04-25

ArXiv (prépublication)

Instance-Conditioned GAN Data Augmentation for Representation Learning

Pietro Astolfi

Arantxa Casanova

Jakob Verbeek

P Vincent

Adriana Romero

2023-03-15

ArXiv (prépublication)

Learning to Substitute Ingredients in Recipes

Bahare Fatemi

Quentin Duval

Rohit Girdhar

Adriana Romero

Recipe personalization through ingredient substitution has the potential to help people meet their dietary needs and preferences, avoid pote… (voir plus)ntial allergens, and ease culinary exploration in everyone's kitchen. To address ingredient substitution, we build a benchmark, composed of a dataset of substitution pairs with standardized splits, evaluation metrics, and baselines. We further introduce Graph-based Ingredient Substitution Module (GISMo), a novel model that leverages the context of a recipe as well as generic ingredient relational information encoded within a graph to rank plausible substitutions. We show through comprehensive experimental validation that GISMo surpasses the best performing baseline by a large margin in terms of mean reciprocal rank. Finally, we highlight the benefits of GISMo by integrating it in an improved image-to-recipe generation pipeline, enabling recipe personalization through user intervention. Quantitative and qualitative results show the efficacy of our proposed system, paving the road towards truly personalized cooking and tasting experiences.

2023-02-14

ArXiv (prépublication)

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

Badr Youbi Idrissi

Diane Bouchacourt

Randall Balestriero

Ivan Evtimov

Caner Hazirbas

Nicolas Ballas

P Vincent

David Lopez-Paz

Mark Ibrahim

Deep learning vision systems are widely deployed across applications where reliability is critical. However, even today's best models can fa… (voir plus)il to recognize an object when its pose, lighting, or background varies. While existing benchmarks surface examples challenging for models, they do not explain why such mistakes arise. To address this need, we introduce ImageNet-X—a set of sixteen human annotations of factors such as pose, background, or lighting the entire ImageNet-1k validation set as well as a random subset of 12k training images. Equipped with ImageNet-X, we investigate 2,200 current recognition models and study the types of mistakes as a function of model’s (1) architecture, e.g. transformer vs. convolutional, (2) learning paradigm, e.g. supervised vs. self-supervised, and (3) training procedures, e.g., data augmentation. Regardless of these choices, we find models have consistent failure modes across ImageNet-X categories. We also find that while data augmentation can improve robustness to certain factors, they induce spill-over effects to other factors. For example, color-jitter augmentation improves robustness to color and brightness, but surprisingly hurts robustness to pose. Together, these insights suggest to advance the robustness of modern vision models, future research should focus on collecting additional data and understanding data augmentation schemes. Along with these insights, we release a toolkit based on ImageNet-X to spur further study into the mistakes image recognition systems make.

2023-01-31

ICLR.cc/2023/Conference (notable)

The Liver Tumor Segmentation Benchmark (LiTS)

Patrick Bilic

Patrick Christ

Eugene Vorontsov

Hongwei Bran Li

Grzegorz Chlebus

Hao Chen

Qi Dou

Chi-Wing Fu

Xu Han

Gabriel Efrain Humpire Mamani

Pheng Ann Heng

Jürgen Hesser

Samuel Kadoury

Julian Walter Holch

Tomasz Konopczynski

Miao Yue

Chunming Li

X. Li

Jana Lipková

John Lowengrub … (voir 99 de plus)

Michal Marianne Amitai

Hans Meine

J. Moltz

Christopher Pal

Marie Piraud

Ivan Ezhov

Xiaojuan Qi

Fernando Navarro

Jin Qi

Florian Kofler

Markus Rempfler

Johannes C. Paetzold

Karsten Roth

Suprosanna Shit

Andrea Schenk

Xiaobin Hu

Anjany Sekuboyina

Ping Zhou

Christian Hülsemeyer

Marcel Beetz

Jan Kirschke

Florian Ettlinger

Felix Gruen

Benedikt Wiestler

Zhiheng Zhang

Georgios Kaissis

Fabian Lohöfer

Rickmer Braren

J. Holch

Michela Antonelli

Felix Hofmann

Woong Bae

Wieland Sommer

Míriam Bellver

Volker Heinemann

Lei Bi

Colin Jacobs

G. Mamani

Bram van Ginneken

Erik B. Dam

Gabriel Chartrand

An Tang

Bogdan Georgescu

Avi Ben-Cohen

Xavier Giró-i-Nieto

Eyal Klang

M. Amitai

E. Konen

Hayit Greenspan

Johan Moreau

Jan Hendrik Moltz

Alexandre Hostettler

Christian Igel

Luc Soler

Fabian Isensee

Refael Vivanti

Paul Jäger

Adi Szeskin

Fucang Jia

Naama Lev-Cohain

Krishna Chaitanya Kaluva

Jacob Sosna

Mahendra Khened

Leo Joskowicz

Ildoo Kim

Bjoern Menze

Jae-Hun Kim

Zengming Shen

Sungwoong Kim

Simon Kohl

Avinash Kori

Ganapathy Krishnamurthi

Fan Li

Hongchao Li

Junbo Li

Xiaomeng Li

Jun Ma

Klaus Maier-Hein

Kevis-Kokitsi Maninis

Dorit Merhof

Akshay Pai

Mathias Perslev

Jens Petersen

Jordi Pont-Tuset

Oliver Rippel

Ignacio Sarasua

Jordi Torres

Christian Wachinger

Chunliang Wang

Leon Weninger

Jianrong Wu

Daguang Xu

Xiaoping Yang

Simon Chun-Ho Yu

Yading Yuan

Liping Zhang

Jorge Cardoso

Spyridon Bakas

2022-11-16

Medical image analysis (publié)

Uncertainty-Driven Active Vision for Implicit Scene Reconstruction

Edward J. Smith

D. Nowrouzezahrai

David Meger

Adriana Romero

2022-10-02

ArXiv (prépublication)

Active 3D Shape Reconstruction from Vision and Touch

Edward J. Smith

David Meger

Luis Pineda

Roberto Calandra

Jitendra Malik

Adriana Romero

Humans build 3D understandings of the world through active object exploration, using jointly their senses of vision and touch. However, in 3… (voir plus)D shape reconstruction, most recent progress has relied on static datasets of limited sensory data such as RGB images, depth maps or haptic readings, leaving the active exploration of the shape largely unexplored. In active touch sensing for 3D reconstruction, the goal is to actively select the tactile readings that maximize the improvement in shape reconstruction accuracy. However, the development of deep learning-based active touch models is largely limited by the lack of frameworks for shape exploration. In this paper, we focus on this problem and introduce a system composed of: 1) a haptic simulator leveraging high spatial resolution vision-based tactile sensors for active touching of 3D objects; 2) a mesh-based 3D shape reconstruction model that relies on tactile or visuotactile signals; and 3) a set of data-driven solutions with either tactile or visuotactile priors to guide the shape exploration. Our framework enables the development of the first fully data-driven solutions to active touch on top of learned models for object understanding. Our experiments show the benefits of such solutions in the task of 3D shape understanding where our models consistently outperform natural baselines. We provide our framework as a tool to foster future research in this direction.

2021-11-08

NeurIPS.cc/2021/Conference (poster)

3D Shape Reconstruction from Vision and Touch

Edward J. Smith

Roberto Calandra

Adriana Romero

Georgia Gkioxari

David Meger

Jitendra Malik

When a toddler is presented a new toy, their instinctual behaviour is to pick it up and inspect it with their hand and eyes in tandem, clear… (voir plus)ly searching over its surface to properly understand what they are playing with. Here, touch provides high fidelity localized information while vision provides complementary global context. However, in 3D shape reconstruction, the complementary fusion of visual and haptic modalities remains largely unexplored. In this paper, we study this problem and present an effective chart-based approach to fusing vision and touch, which leverages advances in graph convolutional networks. To do so, we introduce a dataset of simulated touch and vision signals from the interaction between a robotic hand and a large array of 3D objects. Our results show that (1) leveraging both vision and touch signals consistently improves single-modality baselines; (2) our approach outperforms alternative modality fusion methods and strongly benefits from the proposed chart-based structure; (3) the reconstruction quality increases with the number of grasps provided; and (4) the touch information not only enhances the reconstruction at the touch site but also extrapolates to its local neighborhood.

2019-12-31

Advances in Neural Information Processing Systems 33 (NeurIPS 2020) (publié)

On the Iterative Refinement of Densely Connected Representation Levels for Semantic Segmentation

Arantxa Casanova

Guillem Cucurull

Adriana Romero

Yoshua Bengio

State-of-the-art semantic segmentation approaches increase the receptive field of their models by using either a downsampling path composed … (voir plus)of poolings/strided convolutions or successive dilated convolutions. However, it is not clear which operation leads to best results. In this paper, we systematically study the differences introduced by distinct receptive field enlargement methods and their impact on the performance of a novel architecture, called Fully Convolutional DenseResNet (FC-DRN). FC-DRN has a densely connected backbone composed of residual networks. Following standard image segmentation architectures, receptive field enlargement operations that change the representation level are interleaved among residual networks. This allows the model to exploit the benefits of both residual and dense connectivity patterns, namely: gradient flow, iterative refinement of representations, multi-scale feature combination and deep supervision. In order to highlight the potential of our model, we test it on the challenging CamVid urban scene understanding benchmark and make the following observations: 1) downsampling operations outperform dilations when the model is trained from scratch, 2) dilations are useful during the finetuning step of the model, 3) coarser representations require less refinement steps, and 4) ResNets (by model construction) are good regularizers, since they can reduce the model capacity when needed. Finally, we compare our architecture to alternative methods and report state-of-the-art result on the Camvid dataset, with at least twice fewer parameters.

2018-06-17

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (publié)

Convolutional neural networks for mesh-based parcellation of the cerebral cortex

Guillem Cucurull

Konrad Wagstyl

Arantxa Casanova

Petar Veličković

Estrid Jakobsen

Adriana Romero

Alan C. Evans

Yoshua Bengio

In order to understand the organization of the cerebral cortex, it is necessary to create a map or parcellation of cortical areas. Reconstru… (voir plus)ctions of the cortical surface created from structural MRI scans, are frequently used in neuroimaging as a common coordinate space for representing multimodal neuroimaging data. These meshes are used to investigate healthy brain organization as well as abnormalities in neurological and psychiatric conditions. We frame cerebral cortex parcellation as a mesh segmentation task, and address it by taking advantage of recent advances in generalizing convolutions to the graph domain. In particular, we propose to assess graph convolutional networks and graph attention networks, which, in contrast to previous mesh parcellation models, exploit the underlying structure of the data to make predictions. We show experimentally on the Human Connectome Project dataset that the proposed graph convolutional models outperform current state-of-the-art and baselines, highlighting the potential and applicability of these methods to tackle neuroimaging challenges, paving the road towards a better characterization of brain diseases.

2017-12-31

MIDL.amsterdam/2018/Conference (présentation orale)

Learning Normalized Inputs for Iterative Estimation in Medical Image Segmentation

Gabriel Chartrand

Eugene Vorontsov

Lisa Di Jorio

An Tang

Adriana Romero

Yoshua Bengio

Chris Pal

Samuel Kadoury

In this paper, we introduce a simple, yet powerful pipeline for medical image segmentation that combines Fully Convolutional Networks (FCNs)… (voir plus) with Fully Convolutional Residual Networks (FC-ResNets). We propose and examine a design that takes particular advantage of recent advances in the understanding of both Convolutional Neural Networks as well as ResNets. Our approach focuses upon the importance of a trainable pre-processing when using FC-ResNets and we show that a low-capacity FCN model can serve as a pre-processor to normalize medical input data. In our image segmentation pipeline, we use FCNs to obtain normalized images, which are then iteratively refined by means of a FC-ResNet to generate a segmentation prediction. As in other fully convolutional approaches, our pipeline can be used off-the-shelf on different image modalities. We show that using this pipeline, we exhibit state-of-the-art performance on the challenging Electron Microscopy benchmark, when compared to other 2D methods. We improve segmentation results on CT images of liver lesions, when contrasting with standard FCN methods. Moreover, when applying our 2D pipeline on a challenging 3D MRI prostate segmentation challenge we reach results that are competitive even when compared to 3D methods. The obtained results illustrate the strong potential and versatility of the pipeline by achieving highly accurate results on multi-modality images from different anatomical regions and organs.

2017-11-13

Medical Image Analysis (inconnu)