Video Frame Selection for Reliable Image Caption Generation
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Solution Overview
Problem
Existing methods for generating explanatory sentences from frame images in moving images using generative models are inefficient, leading to a risk of omitting important information, particularly in critical moments like incidents or accidents.
Innovation Solution
An information processing apparatus and method that utilizes a generative model subjected to machine learning to analyze and select frame images for explanatory sentence generation based on analysis results, reducing the likelihood of omitting important information by employing a selection process that prioritizes frames with significant content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If frame images are sampled to reduce processing time, then time required for explanatory sentence generation is reduced, but the possibility of omitting important information increases
Solution Approach 1:
The system performs preliminary analysis of frame images using a detection model before selecting them for explanatory sentence generation. This preliminary action identifies and prioritizes frames containing important information (such as incidents or accidents) so they can be ensured to be processed, resolving the contradiction between time efficiency and reliability by preparing the selection criteria in advance.
Solution Approach 2:
The system incorporates feedback mechanisms where the detection model continuously monitors frame images and provides feedback on which frames contain important information. This feedback loop ensures that frames with significant content are identified and selected for explanatory sentence generation, preventing omission of important information while maintaining efficient processing through targeted selection.
2Reliability
If all frame images are processed to ensure complete analysis, then reliability of information capture is improved, but processing time and computational resources increase
Solution Approach 1:
The system extracts and separates the selection function from the processing function. By using the detection model to identify and extract only the frames containing important information, the system avoids processing all frame images unnecessarily. This extraction approach maintains reliability by focusing on critical frames while improving productivity by excluding redundant frames from full processing.
Solution Approach 2:
The system applies local quality by treating different frame images differently based on their content importance. Frames identified as containing important information receive full processing attention, while frames without significant content are handled differently (skipped or summarized). This localized approach ensures reliable analysis of critical moments while improving overall processing efficiency.
Data Source
AI summary
An information processing apparatus acquires a frame image that is a constituent of a moving image that is an analysis target using a generative model subjected to machine learning in such a way that the generative model is able to generate an explanatory sentence of an image, and analyzes the acquired frame image. The information processing apparatus selects a frame image as a target of which an explanatory sentence is to be generated by the generative model based on an analysis result for the frame image. The information processing apparatus causes the generative model to generate an explanatory sentence of the frame image by using the frame image and an analysis result for the frame image.