Global Modulation

Global modulation refers to techniques that incorporate broad contextual information into models to improve performance across diverse datasets or scenarios. Current research focuses on applying this concept in various domains, including signal processing (e.g., using global pooling and modulation blocks in speech separation and ECG analysis), image generation (e.g., disentangling local and global variations in video editing and image inpainting), and robotics (e.g., coordinating large-scale deployments). These advancements enhance model robustness, efficiency, and accuracy, leading to improved diagnostic tools, more realistic image synthesis, and more effective robotic systems.