113 citations · 263 across the 30 of their papers we have counts for
24 papers · 1 filter
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…
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…
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…
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…
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…
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…
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…
Temporal Multimodal Fusion for Video Emotion Classification in the Wild
Valentin Vielzeuf, Stéphane Pateux, Frédéric Jurie
This paper addresses the question of emotion classification. The task consists in predicting emotion labels (taken among a set of possible labels) best describing the emotions cont…
Generating Visual Representations for Zero-Shot Classification
Maxime Bucher, Stéphane Herbin, Frédéric Jurie
This paper addresses the task of learning an image clas-sifier when some categories are defined by semantic descriptions only (e.g. visual attributes) while the others are defined…
Unsupervised part learning for visual recognition
Ronan Sicre, Yannis Avrithis, Ewa Kijak +1
Part-based image classification aims at representing categories by small sets of learned discriminative parts, upon which an image representation is built. Considered as a promisin…
An Adversarial Regularisation for Semi-Supervised Training of Structured Output Neural Networks
Mateusz Koziński, Loïc Simon, Frédéric Jurie
We propose a method for semi-supervised training of structured-output neural networks. Inspired by the framework of Generative Adversarial Networks (GAN), we train a discriminator…
Automatic discovery of discriminative parts as a quadratic assignment problem
Ronan Sicre, Julien Rabin, Yannis Avrithis +2
Part-based image classification consists in representing categories by small sets of discriminative parts upon which a representation of the images is built. This paper addresses t…
Deep fusion of visual signatures for client-server facial analysis
Binod Bhattarai, Gaurav Sharma, Frederic Jurie
Facial analysis is a key technology for enabling human-machine interaction. In this context, we present a client-server framework, where a client transmits the signature of a face…
Improving Semantic Embedding Consistency by Metric Learning for Zero-Shot Classification
Maxime Bucher, Stéphane Herbin, Frédéric Jurie
This paper addresses the task of zero-shot image classification. The key contribution of the proposed approach is to control the semantic embedding of images -- one of the main ing…
CP-mtML: Coupled Projection multi-task Metric Learning for Large Scale Face Retrieval
Binod Bhattarai, Gaurav Sharma, Frederic Jurie
We propose a novel Coupled Projection multi-task Metric Learning (CP-mtML) method for large scale face retrieval. In contrast to previous works which were limited to low dimensiona…
Local Higher-Order Statistics (LHS) describing images with statistics of local non-binarized pixel patterns
Gaurav Sharma, Frederic Jurie
We propose a new image representation for texture categorization and facial analysis, relying on the use of higher-order local differential statistics as features. It has been rece…
Expanded Parts Model for Semantic Description of Humans in Still Images
Gaurav Sharma, Frederic Jurie, Cordelia Schmid
We introduce an Expanded Parts Model (EPM) for recognizing human attributes (e.g. young, short hair, wearing suit) and actions (e.g. running, jumping) in still images. An EPM is a…
Hybrid multi-layer Deep CNN/Aggregator feature for image classification
Praveen Kulkarni, Joaquin Zepeda, Frederic Jurie +2
Deep Convolutional Neural Networks (DCNN) have established a remarkable performance benchmark in the field of image classification, displacing classical approaches based on hand-ta…