8 citations · 8 across the 7 of their papers we have counts for
8 papers
Where-OPD: Spatially Guided On-Policy Self-Distillation of MLLMs with Synthetic Scenes
Sophia Sirko-Galouchenko, Monika Wysoczanska, Andrei Bursuc +2
On-policy self-distillation has recently emerged as an effective approach for improving language-model reasoning by supervising students with a frozen or EMA version of themselves…
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…
World Engine: Towards the Era of Post-Training for Autonomous Driving
Tianyu Li, Li Chen, Caojun Wang +16
Autonomous vehicles must operate safely in the real world, where errors can have severe consequences. Although modern end-to-end driving policies excel in routine scenarios, their…
V-GIFT: Boosting Visual Instruction Tuning with Self-Supervised Guidance
Sophia Sirko-Galouchenko, Monika Wysoczanska, Andrei Bursuc +2
Multimodal large language models (MLLMs) perform well on many vision-language tasks but often struggle with vision-centric problems that require fine-grained visual perception. Rec…
SuperQuadricOcc: Real-Time Self-Supervised Semantic Occupancy Estimation with Superquadric Volume Rendering
Seamie Hayes, Alexandre Boulch, Andrei Bursuc +4
Self-supervision for semantic occupancy estimation is appealing as it removes the labour-intensive manual annotation, thus allowing one to scale to larger autonomous driving datase…
UnShadowNet: Illumination Critic Guided Contrastive Learning For Shadow Removal
Subhrajyoti Dasgupta, Arindam Das, Senthil Yogamani +4
Shadows are frequently encountered natural phenomena that significantly hinder the performance of computer vision perception systems in practical settings, e.g., autonomous driving…
StyleLess layer: Improving robustness for real-world driving
Julien Rebut, Andrei Bursuc, Patrick Pérez
Deep Neural Networks (DNNs) are a critical component for self-driving vehicles. They achieve impressive performance by reaping information from high amounts of labeled data. Yet, t…
Dynamic Task Weighting Methods for Multi-task Networks in Autonomous Driving Systems
Isabelle Leang, Ganesh Sistu, Fabian Burger +2
Deep multi-task networks are of particular interest for autonomous driving systems. They can potentially strike an excellent trade-off between predictive performance, hardware cons…