Incorporating visual information into the Social-MAE framework can significantly enhance the understanding of social interactions and actions in multi-person scenes. One approach to achieve this is by integrating a multi-modal architecture that can process both motion and visual data simultaneously. This can be done by adding additional input channels for visual features, such as RGB images or depth maps, alongside the existing motion data input.
To effectively combine visual and motion information, the model can utilize fusion techniques like late fusion, early fusion, or attention mechanisms. Late fusion involves processing the visual and motion data separately and then combining the features at a later stage. Early fusion combines the visual and motion data at the input level before passing it through the network. Attention mechanisms can be used to dynamically weigh the importance of visual and motion features at different stages of the network.
Furthermore, pre-training the model on a large-scale dataset that includes both visual and motion data can help the model learn rich representations that capture the complex relationships between visual cues and human behavior. By fine-tuning the pre-trained model on specific social tasks, such as social grouping or action understanding, the model can leverage its learned representations to improve performance on these tasks.