113 citations · 263 across the 30 of their papers we have counts for
36 papers
LATENTPATCH: A Non-Parametric Approach for Face Generation and Editing
Benjamin Samuth, Julien Rabin, David Tschumperlé +1
This paper presents LatentPatch, a new method for generating realistic images from a small dataset of only a few images. We use a lightweight model with only a few thousand paramet…
On the De-duplication of LAION-2B
Ryan Webster, Julien Rabin, Loic Simon +1
Generative models, such as DALL-E, Midjourney, and Stable Diffusion, have societal implications that extend beyond the field of computer science. These models require large image d…
Adversarial Counterfactual Visual Explanations
Guillaume Jeanneret, Loïc Simon, Frédéric Jurie
Counterfactual explanations and adversarial attacks have a related goal: flipping output labels with minimal perturbations regardless of their characteristics. Yet, adversarial att…
Empowering the trustworthiness of ML-based critical systems through engineering activities
Juliette Mattioli, Agnes Delaborde, Souhaiel Khalfaoui +3
This paper reviews the entire engineering process of trustworthy Machine Learning (ML) algorithms designed to equip critical systems with advanced analytics and decision functions.…
Diffusion Models for Counterfactual Explanations
Guillaume Jeanneret, Loïc Simon, Frédéric Jurie
Counterfactual explanations have shown promising results as a post-hoc framework to make image classifiers more explainable. In this paper, we propose DiME, a method allowing the g…
On the inductive biases of deep domain adaptation
Rodrigue Siry, Louis Hémadou, Loïc Simon +1
Domain alignment is currently the most prevalent solution to unsupervised domain-adaptation tasks and are often being presented as minimizers of some theoretical upper-bounds on ri…
Training face verification models from generated face identity data
Dennis Conway, Loic Simon, Alexis Lechervy +1
Machine learning tools are becoming increasingly powerful and widely used. Unfortunately membership attacks, which seek to uncover information from data sets used in machine learni…
This Person (Probably) Exists. Identity Membership Attacks Against GAN Generated Faces
Ryan Webster, Julien Rabin, Loic Simon +1
Recently, generative adversarial networks (GANs) have achieved stunning realism, fooling even human observers. Indeed, the popular tongue-in-cheek website {\small \url{ http://this…
Towards a General Model of Knowledge for Facial Analysis by Multi-Source Transfer Learning
Valentin Vielzeuf, Alexis Lechervy, Stéphane Pateux +1
This paper proposes a step toward obtaining general models of knowledge for facial analysis, by addressing the question of multi-source transfer learning. More precisely, the propo…
n-MeRCI: A new Metric to Evaluate the Correlation Between Predictive Uncertainty and True Error
Michel Moukari, Loïc Simon, Sylvaine Picard +1
As deep learning applications are becoming more and more pervasive in robotics, the question of evaluating the reliability of inferences becomes a central question in the robotics…
Learning 2D to 3D Lifting for Object Detection in 3D for Autonomous Vehicles
Siddharth Srivastava, Frederic Jurie, Gaurav Sharma
We address the problem of 3D object detection from 2D monocular images in autonomous driving scenarios. We propose to lift the 2D images to 3D representations using learned neural…
MFAS: Multimodal Fusion Architecture Search
Juan-Manuel Pérez-Rúa, Valentin Vielzeuf, Stéphane Pateux +2
We tackle the problem of finding good architectures for multimodal classification problems. We propose a novel and generic search space that spans a large number of possible fusion…
Detecting Overfitting of Deep Generative Networks via Latent Recovery
Ryan Webster, Julien Rabin, Loic Simon +1
State of the art deep generative networks are capable of producing images with such incredible realism that they can be suspected of memorizing training images. It is why it is not…
Semantic bottleneck for computer vision tasks
Maxime Bucher, Stéphane Herbin, Frédéric Jurie
This paper introduces a novel method for the representation of images that is semantic by nature, addressing the question of computation intelligibility in computer vision tasks. M…
Multi-Level Sensor Fusion with Deep Learning
Valentin Vielzeuf, Alexis Lechervy, Stéphane Pateux +1
In the context of deep learning, this article presents an original deep network, namely CentralNet, for the fusion of information coming from different sensors. This approach is de…
The Many Moods of Emotion
Valentin Vielzeuf, Corentin Kervadec, Stéphane Pateux +1
This paper presents a novel approach to the facial expression generation problem. Building upon the assumption of the psychological community that emotion is intrinsically continuo…
RPNet: an End-to-End Network for Relative Camera Pose Estimation
Sovann En, Alexis Lechervy, Frédéric Jurie
This paper addresses the task of relative camera pose estimation from raw image pixels, by means of deep neural networks. The proposed RPNet network takes pairs of images as input…
Faster RER-CNN: application to the detection of vehicles in aerial images
Jean Ogier du Terrail, Frédéric Jurie
Detecting small vehicles in aerial images is a difficult job that can be challenging even for humans. Rotating objects, low resolution, small inter-class variability and very large…
Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks
Shivang Agarwal, Jean Ogier Du Terrail, Frédéric Jurie
Object detection-the computer vision task dealing with detecting instances of objects of a certain class (e.g., 'car', 'plane', etc.) in images-attracted a lot of attention from th…
CentralNet: a Multilayer Approach for Multimodal Fusion
Valentin Vielzeuf, Alexis Lechervy, Stéphane Pateux +1
This paper proposes a novel multimodal fusion approach, aiming to produce best possible decisions by integrating information coming from multiple media. While most of the past mult…
An Occam's Razor View on Learning Audiovisual Emotion Recognition with Small Training Sets
Valentin Vielzeuf, Corentin Kervadec, Stéphane Pateux +2
This paper presents a light-weight and accurate deep neural model for audiovisual emotion recognition. To design this model, the authors followed a philosophy of simplicity, drasti…
CAKE: Compact and Accurate K-dimensional representation of Emotion
Corentin Kervadec, Valentin Vielzeuf, Stéphane Pateux +2
Numerous models describing the human emotional states have been built by the psychology community. Alongside, Deep Neural Networks (DNN) are reaching excellent performances and are…
Deep multi-scale architectures for monocular depth estimation
Michel Moukari, Sylvaine Picard, Loic Simon +1
This paper aims at understanding the role of multi-scale information in the estimation of depth from monocular images. More precisely, the paper investigates four different deep CN…
TS-Net: Combining modality specific and common features for multimodal patch matching
Sovann En, Alexis Lechervy, Frédéric Jurie
Multimodal patch matching addresses the problem of finding the correspondences between image patches from two different modalities, e.g. RGB vs sketch or RGB vs near-infrared. The…