Text-Based Editing Component Search Using Shared Embeddings
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Solution Overview
Problem
Existing video editing technologies do not enable users to search for editing components using natural language, lacking an understanding of the relationship between natural text language and editing components.
Innovation Solution
A machine learning model is trained to align visual embeddings of editing components with text embeddings by projecting them into a common space, using a first and second multilayer perceptron (MLP) sub-models, and a contrastive loss function to determine similar editing components based on user queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional search methods are used for editing components, then the search process is simple and direct, but users cannot search using natural language and the search functionality is limited
Solution Approach 1:
The patent introduces an intermediary mechanism that bridges natural language queries and editing component search. A machine learning model serves as this intermediary, translating user-friendly natural language queries into effective search criteria, thereby enabling versatile search without requiring users to directly interact with complex search parameters.
Solution Approach 2:
The patent replaces traditional mechanical search mechanisms with a machine learning-based system. Instead of relying on conventional search algorithms that require specific query formats, the system uses neural networks to understand and process natural language, substituting the mechanical search process with an intelligent interpretation and matching mechanism.
2Ease of operation
If natural language search is implemented, then ease of operation is improved, but the underlying system complexity increases
Solution Approach 1:
The machine learning model performs self-service by automatically learning and adapting to the relationships between natural language queries and editing components. The system self-trains on data to understand semantic meanings, eliminating the need for users to manually configure search parameters or for the system to require complex user input, thereby simplifying operation while managing complexity internally.
Solution Approach 2:
The patent changes the fundamental parameters of the search system by introducing machine learning models that can process natural language. This parameter change allows the system to accept queries in human language rather than structured formats, significantly improving ease of operation. The complexity is managed through automated learning processes that adapt to different query types without requiring manual reconfiguration.
3Measurement precision
If machine learning models are used to align visual and text embeddings, then search accuracy is improved, but training and computational requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models on large datasets of editing components and their associated data before actual search operations. This pre-training phase captures essential patterns and relationships, allowing the model to perform accurate searches during inference without requiring excessive computational resources at query time. The heavy lifting is done in advance during training.
Solution Approach 2:
The patent uses copying by creating simplified representations (embeddings) of editing components that capture their essential characteristics. Instead of processing the full complexity of each editing component during search, the system works with compressed embedding vectors that preserve the necessary information for accurate matching, thereby reducing computational requirements while maintaining search accuracy.
Data Source
AI summary
The present disclosure describes techniques for searching editing components based on text using a machine learning model. A plurality of visual embeddings indicative of a plurality of visual editing components is acquired by the machine learning model. The plurality of visual embeddings indicative of the plurality of visual editing components is projected into a common space by a first sub-model of the machine learning model. A text query input is received by a user. A text embedding indicative of the text query is generated. The text embedding is projected into the common space by a second sub-model of the machine learning model. At least one visual editing component among the plurality of visual editing components is determined based on the projected text embedding and the plurality of projected visual embeddings in the common space. Information indicative of the at least one visual editing component is displayed via a user interface.