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Title: Self-Enhancing Video Data Management System for Compositional Events with Large Language Models
Complex video queries can be answered by decomposing them into modular subtasks. However, existing video data management systems assume the existence of predefined modules for each subtask. We introduce VOCAL-UDF, a novel self-enhancing system that supports compositional queries over videos without the need for predefined modules. VOCAL-UDF automatically identifies and constructs missing modules and encapsulates them as user-defined functions (UDFs), thus expanding its querying capabilities. To achieve this, we formulate a unified UDF model that leverages large language models (LLMs) to aid in new UDF generation. VOCAL UDF handles a wide range of concepts by supporting both program-based UDFs (i.e., Python functions generated by LLMs) and distilled-model UDFs (lightweight vision models distilled from strong pretrained models). To resolve the inherent ambiguity in user intent, VOCAL-UDF generates multiple candidate UDFs and uses active learning to efficiently select the best one. With the self-enhancing capability, VOCAL-UDF significantly improves query performance across three video datasets.  more » « less
Award ID(s):
2211133
PAR ID:
10611949
Author(s) / Creator(s):
; ; ; ;
Publisher / Repository:
ACM DL
Date Published:
Journal Name:
Proceedings of the ACM on Management of Data
Volume:
3
Issue:
3
ISSN:
2836-6573
Page Range / eLocation ID:
1 to 29
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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