Multimodal Loss

Multimodal loss functions are crucial for effectively integrating information from diverse data sources (e.g., images, text, audio) in machine learning models. Current research focuses on improving the robustness and efficiency of multimodal learning by addressing challenges like noisy labels, gradient conflicts between unimodal and multimodal objectives, and imbalanced data. This involves developing novel architectures and algorithms, such as Pareto-based optimization and quality-aware fusion methods, to enhance representation learning and downstream task performance. The resulting advancements have significant implications for various applications, including medical image analysis, robotics, and cross-modal retrieval, by enabling more accurate and reliable models.