The potential trade-offs between the computational efficiency of PoCa and the quality of the generated captions lie in the increased computational cost associated with multiple inference steps and the use of large language models for caption merging. While PoCa enhances caption quality by leveraging hierarchical merging, this can lead to higher computational overhead, especially when processing a large number of images. To optimize this trade-off, one approach could involve fine-tuning an image captioning model on the captions generated by PoCa. By distilling the knowledge captured by PoCa into the fine-tuned model, it would enable a single inference pass during deployment while still benefiting from the enhanced caption quality achieved by PoCa. This approach would help balance computational efficiency with caption quality.