CAST: Training a Student Expert via Semi-Supervised Foundation Model Distillation Pardis Taghavi, Tian Liu, Renjie Li, Reza Langari, Zhengzhong Tu paper | arXiv | project page

• CAST is a semi‐supervised knowledge distillation (SSKD) framework that compresses pretrained vision‐foundation models (VFMs) into compact expert networks by leveraging limited labeled data and abundant unlabeled data via stage‐wise fine‐tuning coupled with a contrastive self‐supervised loss.

SwinMTL: A Shared Architecture for Simultaneous Depth Estimation and Semantic Segmentation from Monocular Camera Images Pardis Taghavi, Reza Langari, Gaurav Pandey code | arXiv

• A simple and effective multi-task learning framework that allows concurrent depth estimation and semantic segmentation using a single camera and without compromising computational efficiency.

The Pulse of Motion: Measuring Physical Frame Rate from Visual Dynamics Xiangbo Gao, Mingyang Wu, Siyuan Yang, Jiongze Yu, Pardis Taghavi, Fangzhou Lin, Zhengzhong Tu arXiv | project page

• Proposes Visual Chronometer, a predictor that recovers Physical Frames Per Second (PhyFPS) from visual dynamics to address chronometric hallucination in generative video models and improve perceived motion naturalness.

NaviDriveVLM: Decoupling High-Level Reasoning and Motion Planning for Autonomous Driving Ximeng Tao, Pardis Taghavi, Dimitar Filev, Reza Langari, Gaurav Pandey arXiv

• A decoupled Navigator-Driver framework that separates semantic reasoning from waypoint prediction, preserving strong VLM reasoning while enabling efficient adaptation for end-to-end motion planning on nuScenes.

AVA: Autonomous Vehicles for All

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• Lead of the Perception Team : Developed a perception system tailored for diverse route scenarios, including both urban and rural environments.

MCITY 2.0 USE CASE AV PERCEPTION

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• 3D point cloud generation and clustering techniques for improved object detection and scene understanding from a single RGB image.
• Validated algorithm outputs through simulation and remote operation on a real autonomous test vehicle at MCity 2.0.