Publications associées à Contextually Plausible and Diverse 3D Human Motion Prediction

MaskCLR: Attention-Guided Contrastive Learning for Robust Action Representation Learning

Alexandre Massoud Alahi, Mariam Ahmed Mahmoud Hegazy Hassan, Mohamed Ossama Ahmed Abdelfattah

Current transformer-based skeletal action recognition models tend to focus on a limited set of joints and low-level motion patterns to predict action classes. This results in significant performance degradation under small skeleton perturbations or changin ...

2024

DiffAirfoil: An Efficient Novel Airfoil Sampler Based on Latent Space Diffusion Model for Aerodynamic Shape Optimization

Pascal Fua, Zhen Wei

Surrogate-based optimization is widely used for aerodynamic shape optimization, and its effectiveness depends on representative sampling of the design space. However, traditional sampling methods are hard-pressed to effectively sample high-dimensional desi ...

2024

Robust Outlier Rejection for 3D Registration with Variational Bayes

Mathieu Salzmann, Zheng Dang, Zhen Wei, Jiancheng Yang, Haobo Jiang

Learning-based outlier (mismatched correspondence) rejection for robust 3D registration generally formulates the outlier removal as an inlier/outlier classification problem. The core for this to be successful is to learn the discriminative inlier/outlier f ...

Ieee Computer Soc2023

Deep Generative Models for Autonomous Driving: from Motion Forecasting to Realistic Image Synthesis

Saeed Saadatnejad

Forecasting is a capability inherent in humans when navigating. Humans routinely plan their paths, considering the potential future movements of those around them. Similarly, to achieve comparable sophistication and safety, autonomous systems must embrace ...

EPFL2023

Smart filter aided domain adversarial neural network for fault diagnosis in noisy industrial scenarios

Olga Fink, Gaëtan Michel Frusque, Baorui Dai, Tianfu Li, Qi Li

The application of unsupervised domain adaptation (UDA)-based fault diagnosis methods has shown significant efficacy in industrial settings, facilitating the transfer of operational experience and fault signatures between different operating conditions, di ...

2023

Learnable Wavelet Transform and Domain Adversarial Learning for Enhanced Bearing Fault Diagnosis

Olga Fink, Gaëtan Michel Frusque, Baorui Dai, Qi Li

The application of unsupervised domain adaptation (UDA)-based fault diagnosis methods has shown significant efficacy in industrial settings, facilitating the transfer of operational experience and fault signatures between different operating conditions, di ...

Research Publishing2023

An Ordinal Latent Variable Model of Conflict Intensity

Robert West

Measuring the intensity of events is crucial for monitoring and tracking armed conflict. Advances in automated event extraction have yielded massive data sets of '' who did what to whom '' micro-records that enable datadriven approaches to monitoring confl ...

Assoc Computational Linguistics-Acl2023

Weakly-supervised Action Transition Learning for Stochastic Human Motion Prediction

Mathieu Salzmann, Wei Mao

We introduce the task of action-driven stochastic human motion prediction, which aims to predict multiple plausible future motions given a sequence of action labels and a short motion history. This differs from existing works, which predict motions that ei ...

IEEE COMPUTER SOC2022

BIGPrior: Towards Decoupling Learned Prior Hallucination and Data Fidelity in Image Restoration

Sabine Süsstrunk, Majed El Helou

Classic image-restoration algorithms use a variety of priors, either implicitly or explicitly. Their priors are hand-designed and their corresponding weights are heuristically assigned. Hence, deep learning methods often produce superior image restoration ...

2022

Bayesian Inversion for Nonlinear Imaging Models Using Deep Generative Priors

Michaël Unser, Thanh-An Michel Pham, Pakshal Narendra Bohra, Jonathan Yuelin Dong

Most modern imaging systems incorporate a computational pipeline to infer the image of interest from acquired measurements. The Bayesian approach to solve such ill-posed inverse problems involves the characterization of the posterior distribution of the im ...

2022

Learning V1 Simple Cells with Vector Representation of Local Content and Matrix Representation of Local Motion

Yufan Ren, Siyuan Huang

This paper proposes a representational model for image pairs such as consecutive video frames that are related by local pixel displacements, in the hope that the model may shed light on motion perception in primary visual cortex (V1). The model couples the ...

ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE2022

Adversarial Robustness by Design Through Analog Computing And Synthetic Gradients

Florent Gérard Krzakala, Ruben Elie Ohana, Julien Marcel Daniel Emmanuel Launay

We propose a new defense mechanism against adversarial at-tacks inspired by an optical co-processor, providing robustness without compromising natural accuracy in both white-box and black-box settings. This hardware co-processor performs a nonlinear fixed ...

IEEE2022

RG-Flow: a hierarchical and explainable flow model based on renormalization group and sparse prior

Dian Wu

Flow-based generative models have become an important class of unsupervised learning approaches. In this work, we incorporate the key ideas of renormalization group (RG) and sparse prior distribution to design a hierarchical flow-based generative model, RG ...

IOP Publishing Ltd2022

Context-Aware Learning for Generative Models

Ricardo Andres Chavarriaga Lozano, José del Rocio Millán Ruiz, Serafeim Perdikis, Robert Leeb

This work studies the class of algorithms for learning with side-information that emerges by extending generative models with embedded context-related variables. Using finite mixture models (FMMs) as the prototypical Bayesian network, we show that maximum- ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

A Shared Representation for Photorealistic Driving Simulators

Alexandre Massoud Alahi, Saeed Saadatnejad, Siyuan Li, Taylor Ferdinand Mordan

A powerful simulator highly decreases the need for real-world tests when training and evaluating autonomous vehicles. Data-driven simulators flourished with the recent advancement of conditional Generative Adversarial Networks (cGANs), providing high-fidel ...

IEEE Institute of Electrical and Electronics Engineers2021

Product of experts for robot learning from demonstration

Emmanuel Pignat

Adaptability and ease of programming are key features necessary for a wider spread of robotics in factories and everyday assistance. Learning from demonstration (LfD) is an approach to address this problem. It aims to develop algorithms and interfaces such ...

EPFL2021

Fast Multi-Step Critiquing for VAE-based Recommender Systems

Boi Faltings, Diego Matteo Antognini

Recent studies have shown that providing personalized explanations alongside recommendations increases trust and perceived quality. Furthermore, it gives users an opportunity to refine the recommendations by critiquing parts of the explanations. On one han ...

ASSOC COMPUTING MACHINERY2021

Conditional LSTM-GAN for Melody Generation from Lyrics

Simon Canales, Yi Yu

Melody generation from lyrics has been a challenging research issue in the field of artificial intelligence and music, which enables us to learn and discover latent relationships between interesting lyrics and accompanying melodies. Unfortunately, the limi ...

2021

Unsupervised Domain Adaptation for Cross-Subject Few-Shot Neurological Symptom Detection

Mahsa Shoaran, Bingzhao Zhu

Modern machine learning tools have shown promise in detecting symptoms of neurological disorders. However, current approaches typically train a unique classifier for each subject. This subject-specific training scheme requires long labeled recordings from ...

IEEE2021

Semantic Perturbations with Normalizing Flows for Improved Generalization

Tatjana Chavdarova, Martin Jaggi, Sebastian Urban Stich

Data augmentation is a widely adopted technique for avoiding overfitting when training deep neural networks. However, this approach requires domain-specific knowledge and is often limited to a fixed set of hard-coded transformations. Recently, several work ...

IEEE2021