  Video question answering (VideoQA) is a complex task that requires diverse
multi-modal data for training. Manual annotation of question and answers for
videos, however, is tedious and prohibits scalability. To tackle this problem,
recent methods consider zero-shot settings with no manual annotation of visual
question-answer. In particular, a promising approach adapts frozen
autoregressive language models pretrained on Web-scale text-only data to
multi-modal inputs. In contrast, we here build on frozen bidirectional language
models (BiLM) and show that such an approach provides a stronger and cheaper
alternative for zero-shot VideoQA. In particular, (i) we combine visual inputs
with the frozen BiLM using light trainable modules, (ii) we train such modules
using Web-scraped multi-modal data, and finally (iii) we perform zero-shot
VideoQA inference through masked language modeling, where the masked text is
the answer to a given question. Our proposed approach, FrozenBiLM, outperforms
the state of the art in zero-shot VideoQA by a significant margin on a variety
of datasets, including LSMDC-FiB, iVQA, MSRVTT-QA, MSVD-QA, ActivityNet-QA,
TGIF-FrameQA, How2QA and TVQA. It also demonstrates competitive performance in
the few-shot and fully-supervised setting. Our code and models are publicly
available at https://github.com/antoyang/FrozenBiLM.
