  We analyze knowledge-based visual question answering, for which given a
question, the models need to ground it into the visual modality and retrieve
the relevant knowledge from a given large knowledge base (KB) to be able to
answer. Our analysis has two folds, one based on designing neural architectures
and training them from scratch, and another based on large pre-trained language
models (LLMs). Our research questions are: 1) Can we effectively augment models
by explicit supervised retrieval of the relevant KB information to solve the
KB-VQA problem? 2) How do task-specific and LLM-based models perform in the
integration of visual and external knowledge, and multi-hop reasoning over both
sources of information? 3) Is the implicit knowledge of LLMs sufficient for
KB-VQA and to what extent it can replace the explicit KB? Our results
demonstrate the positive impact of empowering task-specific and LLM models with
supervised external and visual knowledge retrieval models. Our findings show
that though LLMs are stronger in 1-hop reasoning, they suffer in 2-hop
reasoning in comparison with our fine-tuned NN model even if the relevant
information from both modalities is available to the model. Moreover, we
observed that LLM models outperform the NN model for KB-related questions which
confirms the effectiveness of implicit knowledge in LLMs however, they do not
alleviate the need for external KB.
