Modal Prior

Modal priors, representing information from different data modalities (e.g., text, images, sensor data), are increasingly used to improve the performance of generative models and enhance multimodal understanding in various applications. Current research focuses on mitigating biases introduced by these priors, particularly in large language models and diffusion models, often employing techniques like causal inference, attention mechanisms, and variational autoencoders to achieve better alignment between inputs and outputs. This work is significant because it addresses challenges like hallucinations in multimodal models and enables more accurate and robust inference in tasks such as image super-resolution, material design, and 3D semantic segmentation. The resulting improvements have direct implications for diverse fields, including computer vision, materials science, and healthcare.