Multimodal Embeddings for Media Characterization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing platforms face challenges in efficiently detecting media trends and characterizing media items due to the time-consuming and resource-intensive nature of analyzing large volumes of user-uploaded content.
Innovation Solution
The system employs a computer-implemented method that generates multimodal embeddings by combining video and audio embeddings for each frame of a media item, along with textual embeddings, to determine media characteristics such as trend association, user interest, and quality assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional media analysis methods are used to detect media trends and characterize media items, then measurement precision can be maintained, but productivity deteriorates due to time-consuming and resource-intensive processing
Solution Approach 1:
The patent transforms media items into embedding vectors that capture essential features in a compressed numerical representation. This parameter transformation enables efficient similarity computation while preserving the semantic meaning needed for accurate trend detection and media characterization.
Solution Approach 2:
The patent replaces traditional mechanical analysis methods (frame-by-frame video analysis, audio signal processing) with neural network-based embedding generation. This substitution dramatically reduces computational complexity while maintaining measurement precision through learned feature representations.
2Measurement precision
If comprehensive media analysis is performed to accurately detect media trends and assess user interest, then measurement precision improves, but use of energy deteriorates due to increased computational resources required
Solution Approach 1:
The patent extracts only the most essential features of media items by generating compact embedding vectors that capture the core semantic information. This extraction approach maintains measurement precision for trend detection while significantly reducing the computational energy required compared to analyzing all raw media data.
Solution Approach 2:
The patent segments the media analysis task into distinct components: video embedding generation, audio embedding generation, and characteristic prediction. This segmentation allows each component to be optimized independently, reducing overall energy consumption while maintaining comprehensive analysis accuracy.
3Measurement precision
If detailed media item analysis is performed to determine multiple media characteristics, then measurement precision improves, but loss of time deteriorates due to increased processing duration
Solution Approach 1:
The patent performs preliminary action by generating embedding vectors that pre-process and compress media information before the actual characteristic detection. This preliminary embedding generation enables faster subsequent analysis while preserving the precision needed for accurate media trend detection and characteristic assessment.
Data Source
AI summary
Methods and systems for media item characterization based on multimodal embeddings are provided herein. A media item including a sequence of video frames is identified. A set of video embeddings representing visual features of the sequence of video frames is obtained. A set of audio embeddings representing audio features of the sequence of video frames is obtained. A set of audiovisual embeddings is generated based on the set of video embeddings and the set of audio embeddings. Each of the set of audiovisual embeddings represents a visual feature and an audio feature of a respective video frame of the sequence of video frames. One or more media characteristics associated with the media item are determined based on the set of audiovisual embeddings.