Long video question answering method and system based on visual language model and causal reasoning tree

By using a visual language model and causal reasoning tree-based approach, a training-free long-video question-answering system is constructed, solving the efficiency and accuracy problems of long-video causal question answering and achieving an efficient and interpretable question-answering process.

CN121999415APending Publication Date: 2026-05-08EAST CHINA NORMAL UNIV
0 Cites 0 Cited by

Patent Information

Application Number
CN202610179148.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-02-09
Publication Date
2026-05-08

AI Technical Summary

Technical Problem

Existing long-video question answering technologies suffer from low computational efficiency, blurred focus, and difficulty in modeling causal relationships. Existing models are costly to apply to long videos and have limited generalization capabilities.

Method used

By employing a visual language model and causal reasoning tree-based approach, and through video parsing, causal unit extraction, hierarchical aggregation, and lightweight indexing, a training-free causal reasoning tree is constructed to achieve efficient retrieval and reasoning.

Benefits of technology

It enables structured understanding of long video content, supports efficient and accurate complex question answering, reduces deployment overhead and has plug-and-play capabilities, and the answer generation process has good interpretability.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The invention discloses a long video question-answering method and system based on a visual language model and a causal reasoning tree. The method comprises the following steps: carrying out time sequence segmentation on an input long video; calling a visual language model to generate text description for each fragment; analyzing the text description into a causal unit; leaf nodes are constructed, then intermediate nodes and root nodes are constructed, and a hierarchical causal reasoning tree is formed to construct a lightweight index for each node; user questions are received; and executing a search strategy on the tree by the question and answer agent, and integrating information to generate a final answer. The system comprises three main modules including a video analysis module, a fruit tree construction module and a question and answer agent module, and the steps of the method are completed. According to the method, a training-free framework completely based on the pre-training model is adopted, fine tuning can be carried out, the effect is further improved, the problem that the information retrieval accuracy of a single visual language model in a long video understanding task is low is effectively solved, and the answer accuracy of problems of global and local contents is improved.
Need to check novelty before this filing date? Find Prior Art