Frame Event Feature Fusion

Frame event feature fusion integrates data from conventional cameras (frames) and event cameras (events) to improve computer vision tasks. Current research focuses on developing effective fusion architectures, including transformer-based networks and hierarchical refinement modules, to leverage the complementary strengths of each modality—frames providing rich context and events offering high temporal resolution. This approach addresses limitations of frame-based methods in challenging conditions like high speed or low light, leading to improved performance in applications such as object detection, depth estimation, and motion deblurring. The resulting enhanced robustness and accuracy have significant implications for various fields, including robotics, autonomous driving, and augmented reality.