The field of video understanding and processing is moving towards more efficient and effective methods for handling large amounts of video data. Key trends include the development of models that can handle real-time video processing, such as streaming video understanding and online video interaction. Another area of focus is the improvement of video quality assessment and enhancement techniques, including the use of multimodal approaches that combine visual and audio information. Additionally, researchers are exploring new methods for dataset distillation and compression, which can help reduce the computational costs associated with training and deploying video understanding models. Noteworthy papers in this area include ProVideLLM, which achieves state-of-the-art results on procedural video understanding tasks while reducing memory and compute requirements, and TimeChat-Online, which introduces a novel approach for real-time video interaction and achieves an 82.8% reduction in video tokens while maintaining 98% performance.