Video Timing Labeling Model with Multi-Network Architecture
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Solution Overview
Problem
Current video timing labeling models are limited to producing a single video labeling result, leading to inaccurate feature extraction and matching errors between text and video features, resulting in incomplete or inaccurate timing labeling.
Innovation Solution
A combined timing labeling model incorporating a timing labeling network, feature extraction network, and visual text translation network is used to recognize video segments matching text information, perform feature extraction, and translate video features into corresponding text information, enabling diverse output results and improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single timing labeling network is used, then the device complexity is reduced, but the measurement precision and reliability of video timing labeling deteriorate
Solution Approach 1:
The patent combines three distinct networks (timing labeling network, feature extraction network, and visual text translation network) into a unified timing labeling model. These networks work collaboratively to process video data, extract features, and generate timing labels, thereby improving measurement precision while maintaining manageable complexity through integrated architecture design
Solution Approach 2:
The timing labeling model performs multiple functions simultaneously: it conducts timing labeling, extracts video features, and translates visual information to text. This multi-functionality allows a single model to address multiple aspects of video analysis, improving overall reliability without requiring separate specialized systems
2Ease of manufacture
If a single timing labeling network is used, then the training process is simpler, but the training precision and effectiveness deteriorate
Solution Approach 1:
The patent implements feedback mechanisms where the feature extraction network and visual text translation network provide information back to the timing labeling network. This feedback loop enables iterative refinement of timing labels during training, improving training accuracy through continuous optimization based on extracted features and translation quality
Solution Approach 2:
The training process is segmented into distinct phases corresponding to each network's function. The timing labeling network, feature extraction network, and visual text translation network can be trained sequentially or in combination, allowing for modular training approaches that maintain simplicity while achieving high precision through coordinated optimization
3Loss of information
If a single output result is produced, then the information loss is reduced, but the adaptability and versatility of the system deteriorate
Solution Approach 1:
The timing labeling model generates multiple types of output results simultaneously, including timing labels, extracted video features, and translated text information. This multi-functional output capability enhances system versatility, allowing the same model to serve different application needs without information loss
Solution Approach 2:
The system expands the output from a single timing label to multiple dimensions of information: temporal boundaries, visual features, and textual descriptions. This dimensional expansion provides comprehensive video understanding while maintaining adaptability for various downstream applications through diverse output formats
Data Source
AI summary
The present disclosure provides a video timing labeling method. The method includes: acquiring a video file to be labeled and text information to be inquired; acquiring a video segment matching the text information to be inquired based on a timing labeling network of a timing labeling model; acquiring a video feature of the video segment matching the text information to be inquired based on a feature extraction network of the timing labeling model; acquiring text information corresponding to the video segment labeled in the video file based on a visual text translation network of the timing labeling model; and outputting the video segment matching the text information to be inquired and the text information corresponding to the video segment labeled in the video file based on the timing labeling model.