LazyVLM: Neuro-Symbolic Approach to Video Analytics

• URL: http://arxiv.org/abs/2505.21459v1
• Date: Tue, 27 May 2025 17:31:17 GMT
• Title: LazyVLM: Neuro-Symbolic Approach to Video Analytics
• Authors: Xiangru Jian, Wei Pang, Zhengyuan Dong, Chao Zhang, M. Tamer Özsu,
• Abstract summary: We introduce LazyVLM, a neuro-symbolic video analytics system that provides a user-friendly query interface similar to Vision Language Models.<n> LazyVLM enables users to effortlessly drop in video data and specify complex multi-frame video queries.<n>We demonstrate that LazyVLM provides a robust, efficient, and user-friendly solution for querying open-domain video data at scale.
• Score: 11.31655472476408
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Current video analytics approaches face a fundamental trade-off between flexibility and efficiency. End-to-end Vision Language Models (VLMs) often struggle with long-context processing and incur high computational costs, while neural-symbolic methods depend heavily on manual labeling and rigid rule design. In this paper, we introduce LazyVLM, a neuro-symbolic video analytics system that provides a user-friendly query interface similar to VLMs, while addressing their scalability limitation. LazyVLM enables users to effortlessly drop in video data and specify complex multi-frame video queries using a semi-structured text interface for video analytics. To address the scalability limitations of VLMs, LazyVLM decomposes multi-frame video queries into fine-grained operations and offloads the bulk of the processing to efficient relational query execution and vector similarity search. We demonstrate that LazyVLM provides a robust, efficient, and user-friendly solution for querying open-domain video data at scale.

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