Knowledge-based Visual Question Answering about Named Entities is a challenging task that requires retrieving information from a multimodal Knowledge Base. Named entities have diverse visual representations and are therefore difficult to recognize. We argue that cross-modal retrieval may help bridge the semantic gap between an entity and its depictions, and is foremost complementary with mono-modal retrieval. We provide empirical evidence through experiments with a multimodal dual encoder, namely CLIP, on the recent ViQuAE, InfoSeek, and Encyclopedic-VQA datasets. Additionally, we study three different strategies to fine-tune such a model: mono-modal, cross-modal, or joint training. Our method, which combines mono-and cross-modal retrieval, is competitive with billion-parameter models on the three datasets, while being conceptually simpler and computationally cheaper.

基于知识的视觉问答（VQA）涉及使用多模态知识库的信息检索，命名实体的多样化视觉表现使其难以识别，我们认为跨模态检索可能有助于弥合实体与其描述之间的语义差距，对单模态检索产生互补作用，通过对最近的ViQuAE、InfoSeek和Encyclopedic-VQA数据集上的实验，我们提供了经验证据。此外，我们研究了三种不同的模型微调策略：单模态、跨模态或联合训练。我们的方法结合了单模态和跨模态检索，与三个数据集上的数十亿参数模型相竞争，同时在概念上更简单、计算上更廉价。

基于知识的视觉问答的跨模态检索