Video Time-Effectiveness Classification via Multi-Modal Neural Network
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Solution Overview
Problem
Existing methods for video time-effectiveness classification are prone to inaccurate results due to noise words in text information or insufficient text information in videos.
Innovation Solution
A method for training a video time-effectiveness classification model that extracts image frames, text information, and time-effectiveness sensitivity information from video samples, and uses these features to train a neural network model for accurate classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If time-effectiveness classification is performed based on text information in a video, then the classification process can be simplified, but the classification accuracy deteriorates when there are noise words or insufficient text information
Solution Approach 1:
The patent combines multiple information sources (text information, image frames, and time-effectiveness sensitivity information) into a unified classification system. The neural network model processes these multi-modal inputs together, merging the strengths of each data type while mitigating their individual weaknesses to achieve both operational efficiency and high accuracy.
Solution Approach 2:
The patent creates a composite feature representation by integrating text features, image features, and time-effectiveness sensitivity features. This composite approach allows the model to leverage the complementary characteristics of different data types, resulting in a robust classification system that maintains accuracy even when individual data sources are noisy or insufficient.
2Device complexity
If only text information is used for classification, then the system complexity is reduced, but the reliability of classification results deteriorates due to noise and insufficient information
Solution Approach 1:
The patent designs a multi-functional classification system that can process and utilize multiple types of information (text, images, temporal sensitivity data). This universal approach allows the same neural network model to handle diverse input formats and maintain reliable classification results across different video types, regardless of the availability or quality of individual data sources.
Solution Approach 2:
The patent introduces an intermediary feature extraction and fusion mechanism that bridges the gap between raw multi-modal data and classification decisions. This intermediary layer processes and integrates information from multiple sources before feeding it to the classification model, thereby improving reliability while managing system complexity through structured data transformation.
3Measurement precision
If multi-modal features are integrated for classification, then the classification accuracy is improved, but the system complexity and data processing requirements increase
Solution Approach 1:
The patent segments the complex multi-modal classification task into distinct processing stages: text feature extraction, image frame extraction, time-effectiveness sensitivity feature extraction, and final integration. This segmentation allows each component to be optimized independently while working together, managing overall system complexity through modular design.
Solution Approach 2:
The patent transforms the classification problem by adding a temporal sensitivity dimension to the traditional text-and-image feature space. This dimensional expansion allows the model to capture time-effectiveness characteristics that are not present in static text or image data alone, improving accuracy through enhanced feature representation without proportionally increasing system complexity.
Data Source
AI summary
A training method for a video time-effectiveness classification model, performed by a computer device, includes obtaining video samples; extracting, from the video samples, sample image frames, first text information, and time-effectiveness sensitivity information; extracting image features from the sample image frames, text features from the first text information, and time-effectiveness sensitivity features of the time-effectiveness sensitivity information; and generating a trained video time-effectiveness classification model from an untrained neural network model by training the neural network model based on the image features, the text features, and the time-effectiveness sensitivity features, wherein the trained video time-effectiveness classification model may be configured to receive a video sample as input and output a predicted effective life cycle classification result.