Vehicle-centric Perception via Multimodal Structured Pre-training
Published:Dec 24, 2025 05:00
•1 min read
•ArXiv Vision
Analysis
This paper introduces VehicleMAE-V2, a novel pre-trained large model designed to improve vehicle-centric perception. The core innovation lies in leveraging multimodal structured priors (symmetry, contour, and semantics) to guide the masked token reconstruction process. The proposed modules (SMM, CRM, SRM) effectively incorporate these priors, leading to enhanced learning of generalizable representations. The approach addresses a critical gap in existing methods, which often lack effective learning of vehicle-related knowledge during pre-training. The use of symmetry constraints, contour feature preservation, and image-text feature alignment are promising techniques for improving vehicle perception in intelligent systems. The paper's focus on structured priors is a valuable contribution to the field.
Key Takeaways
- •VehicleMAE-V2 leverages multimodal structured priors for improved vehicle perception.
- •Symmetry, contour, and semantics are used as structured priors.
- •The model aims to learn generalizable representations for vehicle-centric tasks.
Reference
“By exploring and exploiting vehicle-related multimodal structured priors to guide the masked token reconstruction process, our approach can significantly enhance the model's capability to learn generalizable representations for vehicle-centric perception.”