41 citations · 49 across the 28 of their papers we have counts for
29 papers
Spot-the-shift: Evaluating Grounded Image Difference Captioning of Long-term Changes
Benedetta Liberatori, Nermin Samet, Paolo Rota +4
Long-term change understanding from images of the same place revisited over time is a challenging task with applications in map maintenance and urban infrastructure monitoring. Pri…
How Far Can 5,500 Hours of Driving Take You? A Scaling Law Analysis of Video Diffusion Models
Victor Besnier, Anh-Quan Cao, Elias Ramzi +5
Video generation for autonomous driving cannot follow the web-scale route: driving data is expensive to collect, bound by privacy requirements, and cannot be scraped at will, so mo…
Pictura: Perspective-View Self-Play at Scale for Driving
Yuan Yin, Elias Ramzi, Marc Lafon +8
Self-play in simulation produces robust driving policies at scale. Demonstrations of such behavior have been made using privileged vectorized observations such as exact poses and v…
Representation Distribution Matching for One-Step Visual Generation
Lan Feng, Wuyang Li, Eloi Zablocki +2
We elucidate the design space of Representation Distribution Matching (RDM), our name for the paradigm that trains a one-step image generator by matching generated and reference fe…
Test-Time Trajectory Optimization for Autonomous Driving
Yihong Xu, Eloi Zablocki, Yuan Yin +4
End-to-end planners for autonomous driving typically generate a set of candidate trajectories, score each one, and return the highest-scoring candidate. However, the scorer is appl…
Driving on Registers
Ellington Kirby, Alexandre Boulch, Yihong Xu +11
We present DrivoR, a simple and efficient transformer-based architecture for end-to-end autonomous driving. Our approach builds on pretrained Vision Transformers (ViTs) and introdu…
MAD: Motion Appearance Decoupling for efficient Driving World Models
Ahmad Rahimi, Valentin Gerard, Eloi Zablocki +2
Recent video diffusion models generate photorealistic, temporally coherent videos, yet they fall short as reliable world models for autonomous driving, where structured motion and…
NAF: Zero-Shot Feature Upsampling via Neighborhood Attention Filtering
Loick Chambon, Paul Couairon, Eloi Zablocki +3
Vision Foundation Models (VFMs) extract spatially downsampled representations, posing challenges for pixel-level tasks. Existing upsampling approaches face a fundamental trade-off:…
RAP: 3D Rasterization Augmented End-to-End Planning
Lan Feng, Yang Gao, Eloi Zablocki +5
Imitation learning for end-to-end driving trains policies only on expert demonstrations. Once deployed in a closed loop, such policies lack recovery data: small mistakes cannot be…
IPA: An Information-Reconstructive Input Projection Framework for Efficient Foundation Model Adaptation
Yuan Yin, Shashanka Venkataramanan, Tuan-Hung Vu +2
Parameter-efficient fine-tuning (PEFT) methods, such as LoRA, reduce adaptation cost by injecting low-rank updates into pretrained weights. However, LoRA's down-projection is rando…
Halton Scheduler For Masked Generative Image Transformer
Victor Besnier, Mickael Chen, David Hurych +2
Masked Generative Image Transformers (MaskGIT) have emerged as a scalable and efficient image generation framework, able to deliver high-quality visuals with low inference costs. H…
VaViM and VaVAM: Autonomous Driving through Video Generative Modeling
Florent Bartoccioni, Elias Ramzi, Victor Besnier +14
We explore the potential of large-scale generative video models for autonomous driving, introducing an open-source auto-regressive video model (VaViM) and its companion video-actio…
GaussRender: Learning 3D Occupancy with Gaussian Rendering
Loïck Chambon, Eloi Zablocki, Alexandre Boulch +2
Understanding the 3D geometry and semantics of driving scenes is critical for safe autonomous driving. Recent advances in 3D occupancy prediction have improved scene representation…
Towards Generalizable Trajectory Prediction Using Dual-Level Representation Learning And Adaptive Prompting
Kaouther Messaoud, Matthieu Cord, Alexandre Alahi
Existing vehicle trajectory prediction models struggle with generalizability, prediction uncertainties, and handling complex interactions. It is often due to limitations like compl…
PPT: Pretraining with Pseudo-Labeled Trajectories for Motion Forecasting
Yihong Xu, Yuan Yin, Éloi Zablocki +3
Accurately predicting how agents move in dynamic scenes is essential for safe autonomous driving. State-of-the-art motion forecasting models rely on datasets with manually annotate…
GIFT: A Framework Towards Global Interpretable Faithful Textual Explanations of Vision Classifiers
Éloi Zablocki, Valentin Gerard, Amaia Cardiel +3
Understanding the decision processes of deep vision models is essential for their safe and trustworthy deployment in real-world settings. Existing explainability approaches, such a…
ReGentS: Real-World Safety-Critical Driving Scenario Generation Made Stable
Yuan Yin, Pegah Khayatan, Éloi Zablocki +2
Machine learning based autonomous driving systems often face challenges with safety-critical scenarios that are rare in real-world data, hindering their large-scale deployment. Whi…
LLM-wrapper: Black-Box Semantic-Aware Adaptation of Vision-Language Models for Referring Expression Comprehension
Amaia Cardiel, Eloi Zablocki, Elias Ramzi +2
Vision Language Models (VLMs) have demonstrated remarkable capabilities in various open-vocabulary tasks, yet their zero-shot performance lags behind task-specific fine-tuned model…
Annealed Winner-Takes-All for Motion Forecasting
Yihong Xu, Victor Letzelter, Mickaël Chen +2
In autonomous driving, motion prediction aims at forecasting the future trajectories of nearby agents, helping the ego vehicle to anticipate behaviors and drive safely. A key chall…
Valeo4Cast: A Modular Approach to End-to-End Forecasting
Yihong Xu, Éloi Zablocki, Alexandre Boulch +11
Motion forecasting is crucial in autonomous driving systems to anticipate the future trajectories of surrounding agents such as pedestrians, vehicles, and traffic signals. In end-t…
UniTraj: A Unified Framework for Scalable Vehicle Trajectory Prediction
Lan Feng, Mohammadhossein Bahari, Kaouther Messaoud Ben Amor +3
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…
Manipulating Trajectory Prediction with Backdoors
Kaouther Messaoud, Kathrin Grosse, Mickael Chen +3
Autonomous vehicles ought to predict the surrounding agents' trajectories to allow safe maneuvers in uncertain and complex traffic situations. As companies increasingly apply traje…
Reliability in Semantic Segmentation: Can We Use Synthetic Data?
Thibaut Loiseau, Tuan-Hung Vu, Mickael Chen +2
Assessing the robustness of perception models to covariate shifts and their ability to detect out-of-distribution (OOD) inputs is crucial for safety-critical applications such as a…
ManiPose: Manifold-Constrained Multi-Hypothesis 3D Human Pose Estimation
Cédric Rommel, Victor Letzelter, Nermin Samet +4
We propose ManiPose, a manifold-constrained multi-hypothesis model for human-pose 2D-to-3D lifting. We provide theoretical and empirical evidence that, due to the depth ambiguity i…