MambaSeg: Efficient Semantic Segmentation with RGB and Event Data
Analysis
This paper addresses the limitations of traditional semantic segmentation methods in challenging conditions by proposing MambaSeg, a novel framework that fuses RGB images and event streams using Mamba encoders. The use of Mamba, known for its efficiency, and the introduction of the Dual-Dimensional Interaction Module (DDIM) for cross-modal fusion are key contributions. The paper's focus on both spatial and temporal fusion, along with the demonstrated performance improvements and reduced computational cost, makes it a valuable contribution to the field of multimodal perception, particularly for applications like autonomous driving and robotics where robustness and efficiency are crucial.
Key Takeaways
- •Proposes MambaSeg, a novel dual-branch semantic segmentation framework.
- •Employs Mamba encoders for efficient modeling of RGB images and event streams.
- •Introduces the Dual-Dimensional Interaction Module (DDIM) for cross-modal fusion.
- •Achieves state-of-the-art segmentation performance with reduced computational cost.
- •Addresses limitations of traditional methods in challenging conditions.
“MambaSeg achieves state-of-the-art segmentation performance while significantly reducing computational cost.”