The field of computer vision is witnessing a significant shift towards leveraging event-driven data to enhance the robustness and accuracy of various tasks. Researchers are exploring the potential of event cameras to address long-standing challenges such as motion blur, low-light conditions, and limited dynamic range. The integration of event data with traditional RGB inputs is enabling the development of more sophisticated and adaptive computer vision systems. Noteworthy papers in this area include EGS-SLAM, which proposes a novel framework for RGB-D Gaussian Splatting SLAM with events, and E-4DGS, which introduces an event-driven dynamic Gaussian Splatting approach for novel view synthesis. EvTurb is also a notable work, as it presents an event guided turbulence removal framework that leverages high-speed event streams to decouple blur and tilt effects. Additionally, the motion cue fusion network (MCFNet) achieves optimal spatiotemporal alignment and adaptive cross-modal feature fusion for robust object detection in dynamic traffic scenarios. These innovative approaches are paving the way for significant advancements in computer vision, enabling more accurate and robust performance in a wide range of applications.