Search Results for author:
Found 25 papers, 6 papers with code
SimPINNs: Simulation-Driven Physics-Informed Neural Networks for Enhanced Performance in Nonlinear Inverse Problems
This paper introduces a novel approach to solve inverse problems by leveraging deep learning techniques.
Text-to-Image Models for Counterfactual Explanations: a Black-Box Approach
This paper addresses the challenge of generating Counterfactual Explanations (CEs), involving the identification and modification of the fewest necessary features to alter a classifier's prediction for a given image.
Adversarial Counterfactual Visual Explanations
Counterfactual explanations and adversarial attacks have a related goal: flipping output labels with minimal perturbations regardless of their characteristics.
Diffusion Models for Counterfactual Explanations
Counterfactual explanations have shown promising results as a post-hoc framework to make image classifiers more explainable.
On the inductive biases of deep domain adaptation
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 risk in the target domain.
Towards a General Model of Knowledge for Facial Analysis by Multi-Source Transfer Learning
This model outperforms its teacher on novel tasks, achieving results on par with state-of-the-art methods on 15 facial analysis tasks (and domains), at an affordable training cost.
n-MeRCI: A new Metric to Evaluate the Correlation Between Predictive Uncertainty and True Error
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 community.
MFAS: Multimodal Fusion Architecture Search
We tackle the problem of finding good architectures for multimodal classification problems.
Semantic bottleneck for computer vision tasks
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.
Multi-Level Sensor Fusion with Deep Learning
In the context of deep learning, this article presents an original deep network, namely CentralNet, for the fusion of information coming from different sensors.
The Many Moods of Emotion
This paper presents a novel approach to the facial expression generation problem.
MERCI: A NEW METRIC TO EVALUATE THE CORRELATION BETWEEN PREDICTIVE UNCERTAINTY AND TRUE ERROR
One contribution of this article is to draw attention on existing metrics developed in the forecast community, designed to evaluate both the sharpness and the calibration of predictive uncertainty.
RPNet: an End-to-End Network for Relative Camera Pose Estimation
While state-of-the-art systems based on SIFT + RANSAC, are able to recover the translation vector only up to scale, RPNet is trained to produce the full translation vector, in an end-to-end way.
Faster RER-CNN: application to the detection of vehicles in aerial images
Detecting small vehicles in aerial images is a difficult job that can be challenging even for humans.
Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks
Object detection-the computer vision task dealing with detecting instances of objects of a certain class (e. g., 'car', 'plane', etc.)
CentralNet: a Multilayer Approach for Multimodal Fusion
This paper proposes a novel multimodal fusion approach, aiming to produce best possible decisions by integrating information coming from multiple media.
An Occam's Razor View on Learning Audiovisual Emotion Recognition with Small Training Sets
This paper presents a light-weight and accurate deep neural model for audiovisual emotion recognition.
CAKE: Compact and Accurate K-dimensional representation of Emotion
Alongside, Deep Neural Networks (DNN) are reaching excellent performances and are becoming interesting features extraction tools in many computer vision tasks. Inspired by works from the psychology community, we first study the link between the compact two-dimensional representation of the emotion known as arousal-valence, and discrete emotion classes (e. g. anger, happiness, sadness, etc.)
Ranked #22 on
Facial Expression Recognition (FER)
on AffectNet
(Accuracy (7 emotion) metric)
Deep multi-scale architectures for monocular depth estimation
This paper aims at understanding the role of multi-scale information in the estimation of depth from monocular images.
TS-Net: Combining modality specific and common features for multimodal patch matching
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.
Temporal Multimodal Fusion for Video Emotion Classification in the Wild
This paper addresses the question of emotion classification.
Generating Visual Representations for Zero-Shot Classification
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 by exemplar images as well.
An Adversarial Regularisation for Semi-Supervised Training of Structured Output Neural Networks
We propose a method for semi-supervised training of structured-output neural networks.
Hard Negative Mining for Metric Learning Based Zero-Shot Classification
Zero-Shot learning has been shown to be an efficient strategy for domain adaptation.
Improving Semantic Embedding Consistency by Metric Learning for Zero-Shot Classification
This paper addresses the task of zero-shot image classification.
