Multimedia Embedding Models for Accurate Video Risk Classification
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Solution Overview
Problem
Current video classification approaches neglect aspects of video beyond frame-by-frame image analysis, leading to inefficiencies in brand safety solutions and requiring extensive handcrafted logic for new classification categories, which are time-consuming to develop and evaluate.
Innovation Solution
A multimedia understanding model is trained on image, video, audio, and text data to generate a unified embedding, which is applied to machine learning models for classification, search engine applications, and chatbot responses, enabling comprehensive content analysis and efficient category generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If frame-by-frame image analysis is used for video classification, then implementation simplicity is maintained, but classification accuracy deteriorates due to neglecting other video aspects
Solution Approach 1:
The patent combines multiple analysis modalities (frame-by-frame image analysis, audio analysis, text analysis, and metadata analysis) into a unified classification system. The multimedia understanding model integrates these different data types to generate comprehensive embeddings, resolving the contradiction by merging simple frame analysis with more complex modalities to achieve both implementation feasibility and high classification accuracy.
Solution Approach 2:
The multimedia understanding model serves multiple functions: it processes images, audio, text, and metadata simultaneously, and can be applied to various classification tasks. This multi-functional approach allows the system to maintain the simplicity of frame analysis while adding other analysis capabilities through a single unified model, rather than requiring separate specialized systems for each modality.
2Ease of manufacture
If handcrafted logic is used for new classification categories, then interpretability is maintained, but development time increases
Solution Approach 1:
The system enables self-service for creating new classification categories through the unified embedding space. When new content types or categories emerge, the multimedia understanding model automatically generates embeddings for them, and they can be integrated into the classification system without manual feature engineering. The system serves itself by adapting to new categories through the learned embedding representations rather than requiring external handcrafted logic development.
Solution Approach 2:
The patent changes the approach from fixed handcrafted logic parameters to learned embedding parameters. The multimedia understanding model learns optimal parameter representations during training, allowing the system to adapt to new classification categories by learning new parameter configurations rather than requiring manual specification of logical rules for each new category.
3Measurement precision
If comprehensive multimedia analysis is implemented, then classification accuracy improves, but computational resources increase
Solution Approach 1:
The patent segments the multimedia analysis into distinct processing stages: extraction of individual modalities (frames, audio, text, metadata), generation of separate embeddings for each modality, and final integration through the unified multimedia understanding model. This segmentation allows computational resources to be distributed across parallel processing streams, improving efficiency while maintaining comprehensive analysis capabilities.
Solution Approach 2:
The unified embedding space serves as an intermediary that integrates multiple data modalities. Rather than directly processing and combining raw multimedia data which would be computationally intensive, the system uses learned embeddings as intermediate representations that capture essential features in a compressed form, reducing computational complexity while preserving classification accuracy.
Data Source
AI summary
Methods, systems, and media for generating one or more custom models, such as artificial intelligence models or machine learning models, using a multimedia understanding model. More particularly, the multimedia understanding model can be a large foundational model that is trained using image data, video data, audio data, text data, and/or page data extracted from multiple content items, where the multimedia understanding model can generate, for a given content item, a unified embedding for use with one or more machine learning models (e.g., a classification server executing a classification model that classifies the content of the content item, such as a video content item, into each of twelve defined risk categories) and/or applications (e.g., an application that generates groups of content items that represent daily trends, a search engine application that provides matching content items based on text inputs, image inputs, audio inputs, video inputs, etc., a classification application that generates new or additional categories for classifying the content of a content item, etc.).