2 papers
cs.CV2026
Training a Student Expert via Semi-Supervised Foundation Model Distillation
Pardis Taghavi, Tian Liu, Renjie Li +2
Foundation models deliver strong perception but are often too computationally heavy to deploy, and adapting them typically requires costly annotations. We introduce a semi-supervis…
cs.RO2026
NaviDriveVLM: Decoupling High-Level Reasoning and Motion Planning for Autonomous Driving
Ximeng Tao, Pardis Taghavi, Dimitar Filev +2
Vision-language models (VLMs) have emerged as a promising direction for end-to-end autonomous driving (AD) by jointly modeling visual observations, driving context, and language-ba…