The field of event-based computer vision is rapidly advancing, with a focus on developing innovative methods for processing and analyzing event data. Researchers are exploring new approaches to fuse event data with traditional image and video data, enabling more accurate and robust computer vision tasks such as semantic segmentation, anomaly detection, and optical flow estimation. A key challenge in this area is addressing the misalignments between event and traditional data, including temporal, spatial, and modal misalignments. To tackle these challenges, researchers are proposing novel event representations, fusion modules, and learning frameworks that can effectively integrate event data with traditional data. Noteworthy papers in this area include:

• Rethinking RGB-Event Semantic Segmentation with a Novel Bidirectional Motion-enhanced Event Representation, which proposes a novel event representation and fusion framework for semantic segmentation.
• Uncertainty-Weighted Image-Event Multimodal Fusion for Video Anomaly Detection, which presents a principled approach to fuse event and image data for anomaly detection.
• PRE-Mamba: A 4D State Space Model for Ultra-High-Frequent Event Camera Deraining, which introduces a novel point-based event camera deraining framework that fully exploits the spatiotemporal characteristics of raw event and rain.