Prompting Large Language Models to Reformulate Queries for Moment Localization
This work addresses the problem of improving moment localization accuracy for video analysis tasks, but it appears incremental as it builds on existing methods by enhancing query formulation.
The paper tackles the challenge of moment localization in untrimmed videos by reformulating natural language queries into more model-friendly instructions using large language models, aiming to improve localization accuracy, though no concrete results or numbers are provided in the abstract.
The task of moment localization is to localize a temporal moment in an untrimmed video for a given natural language query. Since untrimmed video contains highly redundant contents, the quality of the query is crucial for accurately localizing moments, i.e., the query should provide precise information about the target moment so that the localization model can understand what to look for in the videos. However, the natural language queries in current datasets may not be easy to understand for existing models. For example, the Ego4D dataset uses question sentences as the query to describe relatively complex moments. While being natural and straightforward for humans, understanding such question sentences are challenging for mainstream moment localization models like 2D-TAN. Inspired by the recent success of large language models, especially their ability of understanding and generating complex natural language contents, in this extended abstract, we make early attempts at reformulating the moment queries into a set of instructions using large language models and making them more friendly to the localization models.