Noisy Modality

Noisy modality research addresses the challenges of effectively utilizing multimodal data where some input channels are incomplete, inaccurate, or irrelevant to the task. Current efforts focus on developing robust models that can handle missing or noisy data, often employing techniques like information bottleneck methods to filter out irrelevant information and denoising diffusion models to clean noisy signals, and incorporating strategies for modality fusion and cross-modal knowledge distillation. This work is crucial for improving the reliability and performance of AI systems in various applications, particularly in medical diagnosis, autonomous driving, and other domains where real-world data is inherently imperfect.