Multimedia Classification via Confidence-Based Routing
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Solution Overview
Problem
Current multimedia content classification methods face challenges in accurately classifying content lacking sufficient training data, leading to inaccurate recommendations.
Innovation Solution
The method involves acquiring a category and object category confidence degree of a target object within multimedia content, determining a first multimedia content based on the confidence degree, generating a prompt based on the category, and identifying the category of the first multimedia content using a second identifying model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional classification methods are used for multimedia content, then the classification process is simple and fast, but the classification accuracy deteriorates when training data is insufficient
Solution Approach 1:
The classification system is divided into multiple specialized modules: a confidence degree calculation module that assesses target object recognition reliability, a first classification module for high-confidence content, and a second classification module for low-confidence content. This segmentation allows each module to specialize in specific scenarios, improving overall accuracy without requiring the entire system to be overly complex.
Solution Approach 2:
The patent introduces an intermediary mechanism (confidence degree assessment) between the input multimedia content and the final classification output. This intermediary evaluates the reliability of target object recognition and routes content to appropriate classification pathways, thereby improving accuracy while maintaining manageable system complexity through structured decision-making.
2Measurement precision
If a single classification model is used, then the system complexity is low, but the classification accuracy deteriorates for content with insufficient training data
Solution Approach 1:
The system dynamically adapts its classification approach based on the confidence degree of target object recognition. For high-confidence content, it uses a simpler first classification model, while for low-confidence content, it activates a more sophisticated second classification model. This dynamic adaptation improves accuracy across different scenarios while avoiding the need for a single overly complex model.
Solution Approach 2:
Different classification models are applied to different subsets of content based on their specific characteristics (confidence degree). The first classification model handles content where target objects are clearly identified, while the second classification model handles content with uncertain or ambiguous target objects. This local specialization improves overall accuracy without requiring uniform complexity across all classification tasks.
3Measurement precision
If multiple classification models are employed to improve accuracy, then the classification precision improves, but the system complexity and processing time increase
Solution Approach 1:
The system applies the more complex second classification model only partially - specifically, only to content with low confidence degrees in target object recognition. For the majority of content with high confidence degrees, the simpler first classification model suffices. This partial application of enhanced classification reduces overall processing time while maintaining accuracy improvements where they are most needed.
Data Source
AI summary
The embodiments of the present disclosure provide a method and apparatus for classifying multimedia content, electronic device, and storage medium. The method comprises: acquiring a category and an object category confidence degree of a target object within a multimedia content to be classified; determining a first multimedia content within the multimedia content to be classified based on the object category confidence degree, and determining a prompt based on the category of the target object within the first multimedia content; and identifying a category of the first multimedia content based on the prompt.