The proposed sentence embedding approach can be extended to handle more complex label descriptions by incorporating techniques to process multi-sentence or paragraph-level class definitions. One approach could involve using natural language processing (NLP) models that are capable of understanding and encoding longer text sequences. For multi-sentence descriptions, the text could be concatenated and fed into the language model to generate a single embedding that captures the overall meaning of the class. Alternatively, for paragraph-level descriptions, the model could be designed to handle longer input sequences and generate embeddings that represent the entire paragraph.
Another strategy could involve pre-processing the complex descriptions to extract key information or features that are most relevant for semantic segmentation. This could involve techniques such as text summarization or information extraction to condense the text into a more manageable format for the language model. By focusing on the most salient information in the descriptions, the model can generate embeddings that effectively capture the essence of the class labels.