While RACC demonstrates significant advancements in efficiency and performance for KB-VQA, several potential limitations exist. One major limitation is its reliance on the quality and relevance of the retrieved documents. If the retrieval process yields irrelevant or low-quality documents, the effectiveness of RACC's compression and aggregation strategies may be compromised, leading to suboptimal answers. This issue is particularly pronounced in specialized domains where the knowledge base may be sparse or outdated.
To improve RACC's handling of more challenging or diverse KB-VQA scenarios, several enhancements could be considered. First, integrating a more sophisticated retrieval mechanism that incorporates user feedback or contextual understanding could enhance the relevance of retrieved documents. Implementing a feedback loop where the model learns from previous interactions could refine the retrieval process over time.
Second, expanding the framework to include multi-modal knowledge sources beyond textual and visual documents could enrich the context available for answering questions. For instance, incorporating audio or video data could provide additional layers of information that enhance the model's understanding of complex queries.
Lastly, enhancing the model's ability to reason over the retrieved contexts could improve its performance in scenarios requiring deeper understanding or inference. This could involve integrating reasoning modules that allow the model to synthesize information from multiple sources and draw conclusions based on the aggregated knowledge.