  Knowledge-based Visual Question Answering (VQA) expects models to rely on
external knowledge for robust answer prediction. Though significant it is, this
paper discovers several leading factors impeding the advancement of current
state-of-the-art methods. On the one hand, methods which exploit the explicit
knowledge take the knowledge as a complement for the coarsely trained VQA
model. Despite their effectiveness, these approaches often suffer from noise
incorporation and error propagation. On the other hand, pertaining to the
implicit knowledge, the multi-modal implicit knowledge for knowledge-based VQA
still remains largely unexplored. This work presents a unified end-to-end
retriever-reader framework towards knowledge-based VQA. In particular, we shed
light on the multi-modal implicit knowledge from vision-language pre-training
models to mine its potential in knowledge reasoning. As for the noise problem
encountered by the retrieval operation on explicit knowledge, we design a novel
scheme to create pseudo labels for effective knowledge supervision. This scheme
is able to not only provide guidance for knowledge retrieval, but also drop
these instances potentially error-prone towards question answering. To validate
the effectiveness of the proposed method, we conduct extensive experiments on
the benchmark dataset. The experimental results reveal that our method
outperforms existing baselines by a noticeable margin. Beyond the reported
numbers, this paper further spawns several insights on knowledge utilization
for future research with some empirical findings.
