Cross-scale attention is a mechanism in neural networks that enables dynamic weighting of features from multiple spatial or temporal scales, enhancing the model's ability to capture both fine and coarse-grained information. This approach improves the adaptability and efficiency of models in tasks such as image recognition and natural language processing by integrating context across different levels of detail.