CMTFormer: Marrying Transformer with Hierarchical Information Interaction for RGB-Event Object Detection
arXiv:2606.29136v1 Announce Type: cross Abstract: Event cameras capture sparse brightness changes with high temporal resolution and high dynamic range, compensating for the deficiencies of the conventional RGB frames. However, previous multi-modal fusion techniques typically fail to handle the inherent heterogeneity between RGB frames and event streams, thus easily leading to noise amplification or redundant feature integration during cross-modal fusion. In this paper, we propose a Cross-Modal information inTeraction transFormer, coined as CMTFormer, which hierarchically integrates RGB and eve
The increasing sophistication of AI models and the availability of advanced sensor technology, like event cameras, are enabling novel approaches to computer vision tasks.
Improved object detection in challenging conditions, especially for autonomous systems, directly impacts real-world applications requiring robust perception.
This research proposes a method to more effectively fuse heterogeneous sensor data (RGB and event streams) for object detection, potentially enhancing accuracy and reliability in dynamic environments.
- · Autonomous vehicle developers
- · Robotics companies
- · Surveillance technology providers
- · Traditional RGB-only vision systems
- · Early multi-modal fusion techniques
More robust and reliable object detection for autonomous systems becomes feasible.
Accelerated development and deployment of autonomous agents in complex, low-light, or high-speed scenarios.
Enhanced safety and operational capabilities for everything from self-driving cars to industrial robots and defense applications.
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Read at arXiv cs.AI