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Publications associées (24)

Social-Transmotion: Promptable Human Trajectory Prediction

Alexandre Massoud Alahi, Saeed Saadatnejad, Yang Gao, Kaouther Messaoud Ben Amor

Accurate human trajectory prediction is crucial for applications such as autonomous vehicles, robotics, and surveillance systems. Yet, existing models often fail to fully leverage the non-verbal social cues human subconsciously communicate when navigating ...

2024

Disorder and Halide Distributions in Cesium Lead Halide Nanocrystals as Seen by Colloidal 133Cs Nuclear Magnetic Resonance Spectroscopy

Laura Piveteau

Colloidal nuclear magnetic resonance (cNMR) spectroscopy on inorganic cesium lead halide nanocrystals (CsPbX3 NCs) is found to serve for noninvasive characterization and quantification of disorder within these structurally soft and labile particles. In par ...

Amer Chemical Soc2024

Rigidity-Aware Detection for 6D Object Pose Estimation

Mathieu Salzmann, Yinlin Hu

Most recent 6D object pose estimation methods first use object detection to obtain 2D bounding boxes before actually regressing the pose. However, the general object detection methods they use are ill-suited to handle cluttered scenes, thus producing poor ...

Ieee Computer Soc2023

Radatron: Accurate Detection Using Multi-resolution Cascaded MIMO Radar

Haitham Al Hassanieh, Junfeng Guan

Millimeter wave (mmWave) radars are becoming a more popular sensing modality in self-driving cars due to their favorable characteristics in adverse weather. Yet, they currently lack sufficient spatial resolution for semantic scene understanding. In this pa ...

SPRINGER INTERNATIONAL PUBLISHING AG2022

Pedestrian 3D Bounding Box Prediction

Alexandre Massoud Alahi, Saeed Saadatnejad, Yi Zhou Ju

Safety is still the main issue of autonomous driving, and in order to be globally deployed, they need to predict pedestrians' motions sufficiently in advance. While there is a lot of research on coarse-grained (human center prediction) and fine-grained pre ...

2022

Sound and Complete Verification of Polynomial Networks

Volkan Cevher, Grigorios Chrysos, Fanghui Liu, Elias Abad Rocamora, Mehmet Fatih Sahin

Polynomial Networks (PNs) have demonstrated promising performance on face and image recognition recently. However, robustness of PNs is unclear and thus obtaining certificates becomes imperative for enabling their adoption in real-world applications. Exist ...

2022

Reactive Navigation in Crowds for Non-holonomic Robots with Convex Bounding Shape

Aude Billard, Diego Felipe Paez Granados, David Julian Gonon

This paper describes a novel method for non-holonomic robots of convex shape to avoid imminent collisions with moving obstacles. The method's purpose is to assist navigation in crowds by correcting steering from the robot's path planner or driver. We evalu ...

2021

BPX preconditioners for isogeometric analysis using (truncated) hierarchical B-splines

Rafael Vazquez Hernandez

We present the construction of additive multilevel preconditioners, also known as BPX preconditioners, for the solution of the linear system arising in isogeometric adaptive schemes with (truncated) hierarchical B-splines. We show that the locality of hier ...

2021

Hierarchical B-spline complexes of discrete differential forms

Rafael Vazquez Hernandez

In this paper, we introduce the hierarchical B-spline complex of discrete differential forms for arbitrary spatial dimension. This complex may be applied to the adaptive isogeometric solution of problems arising in electromagnetics and uid mechanics. We de ...

2020

Pedestrian Intention Prediction: A Multi-task Perspective

Alexandre Massoud Alahi, Saeed Saadatnejad

In order to be globally deployed, autonomous cars must guarantee the safety of pedestrians. This is the reason why forecasting pedestrians' intentions sufficiently in advance is one of the most critical and challenging tasks for autonomous vehicles. This w ...

2020

Neural Scene Decomposition for Multi-Person Motion Capture

Pascal Fua, Mathieu Salzmann, Helge Jochen Rhodin, Isinsu Katircioglu, Victor Constantin

Learning general image representations has proven key to the success of many computer vision tasks. For example, many approaches to image understanding problems rely on deep networks that were initially trained on ImageNet, mostly because the learned featu ...

IEEE2019

Improving Spatial Data Processing by Clipping Minimum Bounding Boxes

Anastasia Ailamaki, Eleni Tzirita Zacharatou, Darius Sidlauskas

The majority of spatial processing techniques rely heavily on the idea of approximating each group of spatial objects by their minimum bounding box (MBB). As each MBB is compact to store (requiring only two multi-dimensional points) and intersection tests ...

IEEE2018

Real-Time Seamless Single Shot 6D Object Pose Prediction

Pascal Fua, Bugra Tekin

We propose a single-shot approach for simultaneously detecting an object in an RGB image and predicting its 6D pose without requiring multiple stages or having to examine multiple hypotheses. Unlike a recently proposed single-shot technique for this task [ ...

2018

3D Box Proposals from a Single Monocular Image of an Indoor Scene

Mathieu Salzmann

Modern object detection methods typically rely on bounding box proposals as input. While initially popularized in the 2D case, this idea has received increasing attention for 3D bounding boxes. Nevertheless, existing 3D box proposal techniques all assume h ...

ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE2018

Adaptive isogeometric methods with hierarchical splines: Optimality and convergence rates

Annalisa Buffa

We consider an adaptive isogeometric method (AIGM) based on (truncated) hierarchical B-splines and continue the study of its numerical properties. We prove that our AIGM is optimal in the sense that delivers optimal convergence rates as soon as the solutio ...

2018

Chaining Mutual Information and Tightening Generalization Bounds

Emmanuel Abbé

Bounding the generalization error of learning algorithms has a long history, which yet falls short in explaining various generalization successes including those of deep learning. Two important difficulties are (i) exploiting the dependencies between the h ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2018

Refinement Algorithms for Adaptive Isogeometric Methods with Hierarchical Splines

Rafael Vazquez Hernandez

The construction of suitable mesh configurations for spline models that provide local refinement capabilities is one of the fundamental components for the analysis and development of adaptive isogeometric methods. We investigate the design and implementati ...

2018

Decision Rule Bounds for Two-Stage Stochastic Bilevel Programs

Daniel Kuhn

We study stochastic bilevel programs where the leader chooses a binary here-and-now decision and the follower responds with a continuous wait-and-see-decision. Using modern decision rule approximations, we construct lower bounds on an optimistic version an ...

SIAM Publications2018

Formal Analysis of Distance Bounding with Secure Hardware

Serge Vaudenay, Handan Kilinç Alper

A distance bounding (DB) protocol is a two-party authentication protocol between a prover and a verifier which is based on the distance between the prover and the verifier. It aims to defeat threats by malicious provers who try to convince that they are cl ...

2018

Security of Distance-Bounding: A Survey

Serge Vaudenay, Srdan Capkun, Martin Benjamin, Gildas Avoine, Aslan Tchamkerten, Ioana Cristina Carlson

Distance-bounding protocols allow a verifier to both authenticate a prover and evaluate whether the latter is located in his vicinity. These protocols are of particular interest in contactless systems, e.g. electronic payment or access control systems, whi ...

2018