1 code implementation • 22 Mar 2024 • Lan Feng, Mohammadhossein Bahari, Kaouther Messaoud Ben Amor, Éloi Zablocki, Matthieu Cord, Alexandre Alahi
Vehicle trajectory prediction has increasingly relied on data-driven solutions, but their ability to scale to different data domains and the impact of larger dataset sizes on their generalization remain under-explored.
Ranked #1 on Trajectory Prediction on nuScenes (using extra training data)
1 code implementation • 19 Oct 2023 • Oriane Siméoni, Éloi Zablocki, Spyros Gidaris, Gilles Puy, Patrick Pérez
We propose here a survey of unsupervised object localization methods that discover objects in images without requiring any manual annotation in the era of self-supervised ViTs.
1 code implementation • 15 Jun 2023 • Yihong Xu, Loïck Chambon, Éloi Zablocki, Mickaël Chen, Alexandre Alahi, Matthieu Cord, Patrick Pérez
In fact, conventional forecasting methods are usually not trained nor tested in real-world pipelines (e. g., with upstream detection, tracking, and mapping modules).
1 code implementation • CVPR 2023 • Oriane Siméoni, Chloé Sekkat, Gilles Puy, Antonin Vobecky, Éloi Zablocki, Patrick Pérez
This way, the salient objects emerge as a by-product without any strong assumption on what an object should be.
1 code implementation • CVPR 2023 • Mehdi Zemni, Mickaël Chen, Éloi Zablocki, Hédi Ben-Younes, Patrick Pérez, Matthieu Cord
We conduct a set of experiments on counterfactual explanation benchmarks for driving scenes, and we show that our method can be adapted beyond classification, e. g., to explain semantic segmentation models.
1 code implementation • 27 Jun 2022 • Florent Bartoccioni, Éloi Zablocki, Andrei Bursuc, Patrick Pérez, Matthieu Cord, Karteek Alahari
Recent works in autonomous driving have widely adopted the bird's-eye-view (BEV) semantic map as an intermediate representation of the world.
Ranked #6 on Bird's-Eye View Semantic Segmentation on nuScenes
1 code implementation • 17 Nov 2021 • Paul Jacob, Éloi Zablocki, Hédi Ben-Younes, Mickaël Chen, Patrick Pérez, Matthieu Cord
In this work, we address the problem of producing counterfactual explanations for high-quality images and complex scenes.
1 code implementation • 16 Sep 2021 • Hédi Ben-Younes, Éloi Zablocki, Mickaël Chen, Patrick Pérez, Matthieu Cord
Learning-based trajectory prediction models have encountered great success, with the promise of leveraging contextual information in addition to motion history.
1 code implementation • 8 Sep 2021 • Florent Bartoccioni, Éloi Zablocki, Patrick Pérez, Matthieu Cord, Karteek Alahari
In such a monocular setup, dense depth is obtained with either additional input from one or several expensive LiDARs, e. g., with 64 beams, or camera-only methods, which suffer from scale-ambiguity and infinite-depth problems.
no code implementations • 13 Jan 2021 • Éloi Zablocki, Hédi Ben-Younes, Patrick Pérez, Matthieu Cord
The concept of explainability has several facets and the need for explainability is strong in driving, a safety-critical application.
1 code implementation • 9 Dec 2020 • Hédi Ben-Younes, Éloi Zablocki, Patrick Pérez, Matthieu Cord
In this era of active development of autonomous vehicles, it becomes crucial to provide driving systems with the capacity to explain their decisions.
no code implementations • 9 Nov 2017 • Éloi Zablocki, Benjamin Piwowarski, Laure Soulier, Patrick Gallinari
Representing the semantics of words is a long-standing problem for the natural language processing community.