activity
20232026
most citedProposal-based Temporal Action Localization with Point-level Supervision

1 citations · 1 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV2026

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…

cs.RO2026

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…

cs.CV2026

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…

cs.LG2025

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…

cs.CV2024

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…

cs.LG2024

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…

cs.CV2023★ 1 cited

Proposal-based Temporal Action Localization with Point-level Supervision

Yuan Yin, Yifei Huang, Ryosuke Furuta +1

Point-level supervised temporal action localization (PTAL) aims at recognizing and localizing actions in untrimmed videos where only a single point (frame) within every action inst…