The field of multimodal learning and tracking is witnessing significant developments, with a focus on improving robustness and adaptability in the presence of missing or incomplete modalities. Researchers are exploring innovative solutions to address the challenges posed by multimodal data, including the use of dynamic fusion mechanisms, cross-modal attention, and synergistic prompting strategies. These approaches aim to enhance the performance of multimodal models in various applications, such as visual tracking, text-to-person image matching, and food intake gesture detection. Noteworthy papers in this area include:

• A study on adaptive and robust multimodal tracking, which proposes a flexible framework for handling missing modalities and achieves state-of-the-art performance across multiple benchmarks.
• A framework for partial multi-label learning, which introduces a novel Semantic Co-occurrence Insight Network (SCINet) to capture text-image correlations and enhance semantic alignment.
• A robust multimodal learning framework for intake gesture detection, which combines wearable and contactless sensing modalities to improve detection performance and maintains robustness under missing modality conditions.