Visual Question Answering with Natural Language Explanation (VQA-NLE) task is challenging due to its high demand for reasoning-based inference. Recent VQA-NLE studies focus on enhancing model networks to amplify the model's reasoning capability but this approach is resource-consuming and unstable. In this work, we introduce a new VQA-NLE model, ReRe (Retrieval-augmented natural language Reasoning), using leverage retrieval information from the memory to aid in generating accurate answers and persuasive explanations without relying on complex networks and extra datasets. ReRe is an encoder-decoder architecture model using a pre-trained clip vision encoder and a pre-trained GPT-2 language model as a decoder. Cross-attention layers are added in the GPT-2 for processing retrieval features. ReRe outperforms previous methods in VQA accuracy and explanation score and shows improvement in NLE with more persuasive, reliability.

本研究解决了视觉问答与自然语言解释任务中推理能力不足的问题。提出了ReRe模型，通过利用记忆中的检索信息来生成准确答案和有说服力的解释，避免了复杂网络和额外数据集的依赖。ReRe在视觉问答准确度和解释评分上均优于以往方法，显示出更强的说服力和可靠性。 

基于检索增强的自然语言推理用于可解释的视觉问答