The field of computer vision is rapidly advancing, with a strong focus on event-based vision and low-light imaging. Recent research has shown significant improvements in the development of event-based cameras, which offer high temporal resolution, low latency, and high dynamic range, making them ideal for applications such as motion deblurring, object detection, and visual odometry. Additionally, low-light imaging has become a crucial area of research, with the development of new methods and frameworks for enhancing image quality in challenging lighting conditions. Noteworthy papers in this area include DarkVRAI, which proposes a novel framework for low-light RAW video denoising, and U3LIE, which introduces an efficient framework for unsupervised ultra-high-resolution UAV low-light image enhancement. Other notable papers include EGTM, which presents a novel event-guided turbulence mitigation method, and FocusMamba, which proposes a collaborative sparsification method for RGB-Event object detection. Overall, the field is moving towards more efficient, accurate, and robust methods for event-based vision and low-light imaging, with potential applications in areas such as autonomous driving, surveillance, and robotics.