C3Net: A cross-modal collaborative calibration of features for object detection using frames and events

Key Points

• The method significantly improves object detection accuracy, particularly in challenging lighting conditions.
• C3Net achieves a 15% increase in performance metrics compared to traditional models.
• Using a unique cross-modal feature calibration approach, it combines data from frames and events for optimal results.
• The findings indicate that combining different types of data can lead to better object recognition in complex environments.