Question-Instructed Visual Descriptions for Zero-Shot Video Question
  Answering
David Romero Thamar Solorio
We present Q-ViD, a simple approach for video question answering (video QA),
that unlike prior methods, which are based on complex architectures,
computationally expensive pipelines or use closed models like GPTs, Q-ViD
relies on a single instruction-aware open vision-language model (InstructBLIP)
to tackle videoQA using frame descriptions. Specifically, we create captioning
instruction prompts that rely on the target questions about the videos and
leverage InstructBLIP to obtain video frame captions that are useful to the
task at hand. Subsequently, we form descriptions of the whole video using the
question-dependent frame captions, and feed that information, along with a
question-answering prompt, to a large language model (LLM). The LLM is our
reasoning module, and performs the final step of multiple-choice QA. Our simple
Q-ViD framework achieves competitive or even higher performances than current
state of the art models on a diverse range of videoQA benchmarks, including
NExT-QA, STAR, How2QA, TVQA and IntentQA.