Question-Instructed Visual Descriptions for Zero-Shot Video Question Answering

TL;DR

Q-ViD is a streamlined video question answering approach that uses instruction-aware captioning and large language models, achieving competitive results without complex architectures or closed models.

Contribution

Introduces Q-ViD, a simple open-vocabulary video QA method leveraging frame captioning and LLMs, outperforming many existing models.

Findings

Achieves state-of-the-art or competitive results on multiple benchmarks.

Uses a single instruction-aware vision-language model for captioning.

Employs LLMs for reasoning and final answer selection.

Abstract

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.