Context-Aware Video Retrieval for Accurate Temporal Event Reasoning
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Solution Overview
Problem
Existing vision-language model systems fail to leverage semantic structure of video metadata and contextual relationships, leading to limited understanding of temporal patterns and event continuity, and lack mechanisms for selecting domain-relevant models and retrieving structured context, resulting in reduced accuracy in query-dependent video reasoning.
Innovation Solution
A context-aware video retrieval and inference system that models multimodal queries using video and language models, applies retrieval-augmented generation techniques, and generates natural language responses by coordinating interactions between large language models, vision language models, and embedding subsystems, utilizing graph neural networks and databases to maintain context across short-term and long-term memory.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If direct prompting of large language models is used without leveraging semantic structure of video metadata, then the system is simpler to operate, but the accuracy of video reasoning and understanding of temporal patterns deteriorates
Solution Approach 1:
The patent introduces an intermediary retrieval system that bridges the user's natural language query and the video metadata. This intermediary layer indexes and retrieves semantically relevant video segments based on the query, enabling the LLM to operate simply while accessing structured semantic information for accurate reasoning.
Solution Approach 2:
The system performs preliminary indexing of video metadata into a retrieval database before actual queries are made. This pre-processing creates a structured semantic representation of video content, allowing accurate reasoning to be achieved without complicating the user interaction interface.
2Measurement precision
If mechanisms for selecting domain-relevant models and retrieving structured context are added, then the accuracy of query-dependent video reasoning is improved, but the device complexity increases
Solution Approach 1:
The patent segments the video processing system into distinct functional modules: a retrieval module for accessing structured context, a selection module for choosing domain-relevant models, and a generation module for producing responses. This segmentation allows each component to be optimized independently, managing overall system complexity while improving reasoning accuracy.
Solution Approach 2:
The retrieval system is designed as a universal component that serves multiple functions: it indexes video metadata, retrieves relevant segments, and provides structured context to the LLM. This multi-functionality reduces the need for separate specialized components, thereby managing complexity while enhancing accuracy.
3Productivity
If retrieval-augmented generation techniques are applied with multiple models orchestrated, then the productivity of context-aware analysis is improved, but the loss of time in query processing increases
Solution Approach 1:
The system performs preliminary retrieval and indexing of video metadata in advance, so that when queries arrive, the relevant context is already prepared and can be immediately accessed. This pre-processing reduces the time required during actual query processing while maintaining high productivity through comprehensive context-aware analysis.
Data Source
AI summary
Various examples, systems, and methods are disclosed relating to an agentic curation pipeline. One system can process questions and other inquiries about video content by using a combination of models and stored information. The system can receive a query related to an event in a video, selects relevant portions of the video using embeddings, and apply the selected video data and a related sub-query to a video model. The output from the video model can be used by a language model, along with stored context, to generate an answer to the original query. The system can returns the answer to the requester.