A Video Is Worth 4096 Tokens: Verbalize Story Videos To Understand Them
  In Zero Shot
Authors: name: Aanisha Bhattacharya, name: Yaman K Singla, name: Balaji Krishnamurthy, name: Rajiv Ratn Shah, name: Changyou Chen

Multimedia content, such as advertisements and story videos, exhibit a rich
blend of creativity and multiple modalities. They incorporate elements like
text, visuals, audio, and storytelling techniques, employing devices like
emotions, symbolism, and slogans to convey meaning. While previous research in
multimedia understanding has focused mainly on videos with specific actions
like cooking, there is a dearth of large annotated training datasets, hindering
the development of supervised learning models with satisfactory performance for
real-world applications. However, the rise of large language models (LLMs) has
witnessed remarkable zero-shot performance in various natural language
processing (NLP) tasks, such as emotion classification, question-answering, and
topic classification. To bridge this performance gap in multimedia
understanding, we propose verbalizing story videos to generate their
descriptions in natural language and then performing video-understanding tasks
on the generated story as opposed to the original video. Through extensive
experiments on five video-understanding tasks, we demonstrate that our method,
despite being zero-shot, achieves significantly better results than supervised
baselines for video understanding. Further, alleviating a lack of story
understanding benchmarks, we publicly release the first dataset on a crucial
task in computational social science, persuasion strategy identification.