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Multimedia content, such as advertisements and story videos, exhibit a richblend of creativity and multiple modalities. They incorporate elements liketext, visuals, audio, and storytelling techniques, employing devices likeemotions, symbolism, and slogans to convey meaning. There is a dearth of largeannotated training datasets in the multimedia domain hindering the developmentof supervised learning models with satisfactory performance for real-worldapplications. On the other hand, the rise of large language models (LLMs) haswitnessed remarkable zero-shot performance in various natural languageprocessing (NLP) tasks, such as emotion classification, question-answering, andtopic classification. To leverage such advanced techniques to bridge thisperformance gap in multimedia understanding, we propose verbalizing long videosto generate their descriptions in natural language, followed by performingvideo-understanding tasks on the generated story as opposed to the originalvideo. Through extensive experiments on fifteen video-understanding tasks, wedemonstrate that our method, despite being zero-shot, achieves significantlybetter results than supervised baselines for video understanding. Furthermore,to alleviate a lack of story understanding benchmarks, we publicly release thefirst dataset on a crucial task in computational social science on persuasionstrategy identification.

A Video Is Worth 4096 Tokens: Verbalize Videos To Understand Them In Zero Shot