ROI Tile Processing for Large Multimodal Model Prompts
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Solution Overview
Problem
Large multimodal models (LMMs) inefficiently process entire media files for summary generation, leading to unnecessary computing costs and resource usage on regions not necessary for providing summary responses.
Innovation Solution
A system and method that allows users to specify a region of interest (ROI) within media files, segmenting and tokenizing only those regions for processing by LMMs, reducing unnecessary computation and enabling higher throughput and lower latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the LMM processes the entirety of the media file to provide summary passages, then the summary passage can be comprehensive and accurate, but the computing cost and processing power consumption increase unnecessarily
Solution Approach 1:
The media file is divided into multiple regions or segments, and the system identifies and processes only the relevant segments containing information necessary for generating the summary passage. This segmentation approach maintains summary accuracy while reducing computing cost by excluding unnecessary regions from processing.
Solution Approach 2:
The system extracts and isolates specific regions of interest from the media file that contain essential information for the summary. By taking out only the necessary portions for processing, the system maintains comprehensive summary accuracy while significantly reducing computing resource consumption compared to processing the entire file.
2Reliability
If the LMM processes the entirety of the media file, then all information can be analyzed, but the processing time and throughput efficiency decrease
Solution Approach 1:
The media file is segmented into multiple regions, and the system processes only the segments containing information necessary for the summary. This reduces the total processing time while maintaining information completeness through selective processing of relevant segments.
Solution Approach 2:
The system extracts and processes only the essential regions containing information needed for the summary passage. This extraction approach maintains information completeness for the summary while significantly improving processing throughput by excluding unnecessary regions from analysis.
3Reliability
If the LMM processes the entirety of the media file, then comprehensive analysis is achieved, but the processing capacity and resource utilization efficiency decrease
Solution Approach 1:
The media file is divided into segments, and the system analyzes only the segments containing necessary information for the summary. This maintains analysis comprehensiveness for relevant content while improving processing capacity utilization by avoiding unnecessary analysis of irrelevant regions.
Solution Approach 2:
The system extracts and processes only the essential regions containing information needed for comprehensive summary analysis. This approach maintains analysis comprehensiveness for the necessary content while significantly improving processing capacity utilization by excluding unnecessary regions from processing.
Data Source
AI summary
A method for processing a multimodal prompt. The method includes receiving a multimodal prompt including a media file and information related to a region of interest (ROI) of the media file. The method further includes determining a ROI of the media file based on the information related to the media file and generating a plurality of media tiles of interest associated with the ROI. The method further includes encoding the plurality of media tiles of interest and using a large multimodal model (LMM) to process the encoded plurality of media tiles of interest according to a natural-language input of the prompt to generate a response.