In order to handle more complex real-world scenarios with diverse object-action interactions, the self-explainable affordance learning approach can be extended in several ways:
Enhanced Model Architecture: The model can be further developed to incorporate more sophisticated self-explainable modules that can capture a wider range of object-action interactions. This can involve utilizing more advanced language models and vision transformers to improve the understanding and generation of embodied captions.
Multi-Modal Fusion: Integrating additional modalities such as audio or tactile feedback can provide a more comprehensive understanding of the environment and improve the accuracy of object-action predictions. By fusing information from multiple sources, the model can better interpret complex scenarios.
Contextual Understanding: Implementing contextual understanding mechanisms can help the model grasp the nuances of different situations and adapt its predictions accordingly. This can involve incorporating contextual information from previous interactions or leveraging external knowledge bases to enhance the interpretability of the model.
Transfer Learning: Leveraging transfer learning techniques can enable the model to generalize better to unseen scenarios by learning from a diverse range of data sources. By pre-training on a wide variety of object-action interactions, the model can adapt more effectively to new and complex scenarios.