Gradient Modulation

Gradient modulation techniques aim to optimize the training process in multimodal learning by dynamically adjusting the influence of different modalities on the model's learning. Current research focuses on addressing modality imbalance and unreliability, often employing adaptive gradient modulation strategies within various model architectures, including those based on autoencoders, attention mechanisms, and contrastive learning. These advancements improve the robustness and performance of multimodal models across diverse applications, such as medical diagnosis, face anti-spoofing, and audio-visual video parsing, by ensuring more balanced and effective utilization of information from all input sources. The resulting improvements in accuracy and efficiency have significant implications for various fields.