  Knowledge-based Visual Qustion-answering (K-VQA) necessitates the use of
background knowledge beyond what is depicted in the image. Current zero-shot
K-VQA methods usually translate an image to a single type of textual decision
context and use a text-based model to answer the question based on it, which
conflicts with the fact that K-VQA questions often require the combination of
multiple question-answering strategies. In light of this, we propose
Rationale-based Ensemble of Answer Context Tactics (REACT) to achieve a dynamic
ensemble of multiple question-answering tactics, comprising Answer Candidate
Generation (ACG) and Rationale-based Strategy Fusion (RSF). In ACG, we generate
three distinctive decision contexts to provide different strategies for each
question, resulting in the generation of three answer candidates. RSF generates
automatic and mechanistic rationales from decision contexts for each candidate,
allowing the model to select the correct answer from all candidates. We conduct
comprehensive experiments on the OK-VQA and A-OKVQA datasets, and our method
significantly outperforms state-of-the-art LLM-based baselines on all datasets.
