The field of multimodal learning is moving towards developing more robust and flexible models that can handle incomplete or missing data. Researchers are exploring new frameworks and techniques to improve the performance of multimodal models in real-world scenarios. A key direction is the development of methods that can learn from multiple modalities and adapt to new or missing modalities. Another area of focus is the improvement of recommendation systems, with several papers proposing new approaches to enhance the performance of multimodal recommender systems. Notable papers include Harmony, which introduces a unified framework for modality incremental learning, and SURE, which enhances multimodal pretraining with missing modalities through uncertainty estimation. Other innovative papers include CoCoRec, which uses consensus-aware contrastive learning for group recommendation, and GUIDER, which proposes a universal framework for denoising multi-modal recommender systems.