The recent advancements in multimodal learning and anomaly detection have shown significant progress, particularly in handling missing data and improving model robustness. In the realm of multimodal learning, there is a notable shift towards developing frameworks that can flexibly incorporate arbitrary modality combinations, addressing the limitations of existing models that often rely on complete data or a single modality. These new approaches, such as the Flexible Mixture-of-Experts (Flex-MoE), demonstrate enhanced performance in scenarios with missing modalities by integrating observed and missing data effectively. Additionally, innovative methods like the Multi-Modal Contrastive Knowledge Distillation (MM-CKD) offer a computationally efficient solution for multimodal sentiment analysis by leveraging cross-modal and cross-sample knowledge without the need for imputation.

In anomaly detection, the focus has been on improving latent space separation and ensemble methods to enhance the accuracy of detecting anomalies in data with known inlier classes. The Conditional Latent space Variational Autoencoder (CL-VAE) stands out by conditioning on data information to fit unique prior distributions for each class, leading to a more interpretable latent space and increased accuracy in anomaly detection.

Noteworthy papers include Flex-MoE for its innovative approach to handling arbitrary modality combinations and CL-VAE for its significant improvements in anomaly detection accuracy.