The proposed ensemble approach can be extended to handle larger and more diverse datasets of image triples by implementing several strategies. First, increasing the number of reference images in the context-sensitive (CS) training sets can enhance the model's ability to generalize across various contexts. This can be achieved by leveraging data augmentation techniques to create synthetic variations of existing images, thereby enriching the dataset without the need for extensive manual labeling.
Second, incorporating a more sophisticated sampling strategy for selecting candidate images (A and B) can improve the diversity of the training data. Instead of random selection, a stratified sampling approach could be employed to ensure that the candidates represent a wide range of categories and contexts, thus capturing a broader spectrum of semantic relationships.
Third, the ensemble model can be designed to dynamically adapt to the characteristics of the dataset. By integrating meta-learning techniques, the model can learn to adjust its parameters based on the specific distribution of the new dataset, allowing it to maintain high performance even as the data characteristics change.
Finally, utilizing transfer learning from pre-trained models on large-scale datasets can provide a strong initialization for the ensemble models. This would allow the models to leverage existing knowledge about image similarity, which can be fine-tuned on the new, larger dataset, thus improving performance and reducing the need for extensive labeled data.