To extend the GS2P model for handling multimodal search queries, several strategies can be employed. First, the model can be adapted to incorporate various data types by integrating a multimodal feature extraction mechanism. This could involve using convolutional neural networks (CNNs) for image and video data, alongside natural language processing (NLP) techniques for text data. By employing a unified representation learning framework, the model can learn to extract and fuse features from different modalities effectively.
Second, the GS2P architecture can be modified to include attention mechanisms that allow the model to weigh the importance of different modalities based on the context of the query. For instance, when a user inputs a query that includes both text and an image, the model can prioritize the features from the modality that is more relevant to the specific search context.
Additionally, the generative semi-supervised learning approach can be expanded to generate pseudo-labels for multimodal data. This would involve training the model on a diverse dataset that includes various combinations of text, images, and videos, thereby enhancing its ability to generalize across different types of queries.
Finally, the incorporation of cross-modal retrieval techniques can further enhance the GS2P model's performance. By leveraging techniques such as cross-modal embeddings, the model can learn to retrieve relevant content from one modality based on queries from another, thus improving the overall search experience for users.