Key Frame Selection for Video Query Latency Reduction
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Solution Overview
Problem
Existing generative models, such as large language models, face challenges in efficiently processing and responding to user queries related to videos due to computational impracticality and high latency, especially when dealing with vast amounts of video data.
Innovation Solution
The implementation of a vision-language model that processes key frames from a video, rather than all frames, to generate responsive content. This approach reduces computational load and latency by selecting a subset of key frames based on visual differences or other criteria, allowing for more accurate and timely responses to user queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all frames of the video are processed using a vision-language model, then the accuracy and completeness of the response to user queries is improved, but the computational load and latency become impracticably high
Solution Approach 1:
The video is segmented into key frames that capture essential visual information, separating the most important frames from redundant ones. This allows the system to process only the necessary frames (key frames) rather than all frames, reducing computational load while maintaining response accuracy.
Solution Approach 2:
The system extracts and processes only the most critical frames (key frames) from the video, removing unnecessary frames from the processing pipeline. This extraction approach maintains the essential visual context needed for accurate query responses while significantly reducing the total number of frames processed.
2Measurement precision
If all frames of the video are processed using a vision-language model, then the accuracy and completeness of the response to user queries is improved, but the computational resources required become excessively high
Solution Approach 1:
The video processing task is segmented by identifying and processing only key frames that contain essential visual information. This segmentation reduces the total number of frames requiring computational resources, thereby lowering energy consumption while maintaining response accuracy.
Solution Approach 2:
The system extracts only the necessary key frames from the complete video sequence, removing redundant frames that would consume computational resources without contributing significantly to query accuracy. This extraction maintains essential visual context while reducing overall computational resource requirements.
3Loss of time
If key frames are selected from the video prior to receiving the user query, then the latency in generating content is reduced, but the ability to respond to specific query contexts may be compromised
Solution Approach 1:
Key frames are selected and processed in advance before receiving user queries, preparing visual information ahead of time. This preliminary action reduces latency when queries arrive, as the system can quickly process the pre-selected key frames rather than processing the entire video sequence in real-time.
Solution Approach 2:
The system uses feedback from user queries to dynamically select and process relevant key frames. When a query is received, the system identifies which pre-selected key frames are most relevant to the query context and processes those specifically, ensuring both low latency and high contextual adaptability.
Data Source
AI summary
Implementations utilize a LLM to generate content responsive to a user query directed to a video and cause audio data for the generated content to be rendered as a response to the user query. Implementations extract a subset of frames from all frames of the video as key frame(s) for the video, and utilize a vision-language model in generating a natural language description for the key frame(s) of the video. A prompt can be generated based on a transcription of the user query and based on the natural language description for the key frame(s) of the video. The prompt is processed as input, using the LLM, to generate the content responsive to the user query directed to the video.