Multi-Modal Feature Recognition for Reliable Audio-Video Tagging
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional automated systems for tagging audio-video content are unreliable when classifying features that require more than one content mode, making manual tagging impracticable for large volumes of content.
Innovation Solution
A system and method for multi-modal content-based automated feature recognition using a computing platform with multi-modal mixer software, feature locator software, and machine learning models to analyze video, audio, and text components, enabling accurate feature identification across multiple media modes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional automated tagging systems are used for multi-modal content, then productivity is improved, but reliability deteriorates
Solution Approach 1:
The patent combines multiple independent automated tagging systems (video analyzer, audio analyzer, text analyzer) into a unified multi-modal system. Each modality processes its content independently and contributes to a comprehensive feature classification, resolving the reliability issue by ensuring that features requiring multiple modes are correctly identified through the integration of all analytical perspectives.
Solution Approach 2:
The system creates a universal tagging platform that handles multiple content modes (video, audio, text) and feature types (objects, actions, emotions, scenes) through a single integrated architecture. The feature classification module universally processes inputs from all modalities, enabling reliable classification of complex features that span multiple modes while maintaining high productivity.
2Reliability
If manual tagging is used to ensure accuracy, then reliability is improved, but productivity deteriorates
Solution Approach 1:
The system implements self-service through automated analysis across all three modalities. The video analyzer, audio analyzer, and text analyzer work autonomously to extract features and generate classifications without human intervention. The system serves itself by integrating results from multiple independent analyses, achieving both high reliability and productivity simultaneously.
3Device complexity
If single-mode automated tagging is used, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent segments the tagging system into three independent analyzers (video, audio, text), each handling a specific modality with dedicated processing logic. This segmentation maintains relatively simple individual components while achieving high measurement precision through the integration of all segments in the feature classification module, which synthesizes results across modalities.
Data Source
AI summary
A system includes a computing platform having processing hardware, and a memory storing software code and a machine learning (ML) model-based feature classifier. When executed, the software code receives media content including a first media component corresponding to a first media mode and a second media component corresponding to a second media mode, encodes the first media component using a first encoder to generate multiple first embedding vectors, and encodes the second media component using a second encoder to generate multiple second embedding vectors. The software code further combines the first embedding vectors and the second embedding vectors to provide an input data structure for a neural network mixer, process, using the neural network mixer, the input data structure to provide feature data corresponding to a feature of the media content, and predict, using the ML model-based feature classifier and the feature data, a classification of the feature.