Generate then Select: Open-ended Visual Question Answering Guided by
  World Knowledge
Xingyu Fu Sheng Zhang Gukyeong Kwon Pramuditha Perera Henghui Zhu Yuhao Zhang Alexander Hanbo Li William Yang Wang Zhiguo Wang Vittorio Castelli Patrick Ng Dan Roth Bing Xiang
The open-ended Visual Question Answering (VQA) task requires AI models to
jointly reason over visual and natural language inputs using world knowledge.
Recently, pre-trained Language Models (PLM) such as GPT-3 have been applied to
the task and shown to be powerful world knowledge sources. However, these
methods suffer from low knowledge coverage caused by PLM bias -- the tendency
to generate certain tokens over other tokens regardless of prompt changes, and
high dependency on the PLM quality -- only models using GPT-3 can achieve the
best result.
  To address the aforementioned challenges, we propose RASO: a new VQA pipeline
that deploys a generate-then-select strategy guided by world knowledge for the
first time. Rather than following the de facto standard to train a multi-modal
model that directly generates the VQA answer, RASO first adopts PLM to generate
all the possible answers, and then trains a lightweight answer selection model
for the correct answer. As proved in our analysis, RASO expands the knowledge
coverage from in-domain training data by a large margin. We provide extensive
experimentation and show the effectiveness of our pipeline by advancing the
state-of-the-art by 4.1% on OK-VQA, without additional computation cost. Code
and models are released at http://cogcomp.org/page/publication_view/1010