Visual Augmentation Embeddings for Accurate Effect Retrieval
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Solution Overview
Problem
Conventional systems fail to generate vector representations of visual augmentations that accurately capture the effects of these augmentations, leading to limited utility in identifying, comparing, or ranking them within interaction systems.
Innovation Solution
A machine learning model is trained using self-supervised learning to generate embeddings that represent visual augmentations by minimizing loss between transformed and original representations, ensuring similar augmentations have similar embeddings and dissimilar ones have distinct embeddings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems generate vector representations of visual augmentations, then the representations can be used for identification and comparison, but the representations fail to accurately capture the effects of the augmentations
Solution Approach 1:
The patent employs self-supervised learning with a feedback mechanism where the model generates embeddings for visual augmentations and then uses these embeddings to predict the original augmentations. The loss between the predicted and actual augmentations is calculated and fed back to update the model parameters, creating a closed-loop system that continuously improves representation accuracy.
Solution Approach 2:
The system uses self-supervised learning where the model trains on its own generated embeddings without requiring external labeled data. The model creates its own training signals by predicting the original augmentations from the generated embeddings, allowing the system to improve its representation capabilities autonomously.
2Measurement precision
If machine learning models are trained to generate embeddings for visual augmentations, then accurate representations can be achieved, but computational resources and training time are consumed
Solution Approach 1:
The patent pre-computes and stores embeddings for a comprehensive set of visual augmentations during an initial training phase. This preliminary action allows the system to quickly retrieve and compare embeddings during actual operation without requiring real-time computation, significantly reducing ongoing computational resource consumption.
Solution Approach 2:
The system creates vector representations (embeddings) that are simplified copies of the original visual augmentations. These compressed representations capture the essential features of the augmentations in a lower-dimensional space, enabling efficient storage, comparison, and retrieval operations that consume fewer computational resources than processing the original visual data.
3Adaptability or versatility
If embeddings are generated for all visual augmentations, then comprehensive identification and comparison capabilities are achieved, but the complexity of the system increases
Solution Approach 1:
The patent extracts the essential features of visual augmentations into separate vector representations (embeddings), separating the augmentation information from the background content. This extraction allows the system to handle and compare only the relevant augmentation features, simplifying the overall processing complexity while maintaining comprehensive identification capabilities.
Solution Approach 2:
The system segments the visual content into distinct components: the background image/video and the visual augmentation. By processing and representing these components separately through their respective embeddings, the system reduces the complexity of analyzing the complete visual data while maintaining the ability to identify and compare augmentations accurately.
Data Source
AI summary
An input video item that includes a target visual augmentation is accessed. A machine learning model uses the input video item to generate an embedding. The embedding may comprise a vector representation of a visual effect of the target visual augmentation. The machine learning model is trained, in an unsupervised training phase, to minimize loss between training video representations generated within each of a plurality of training sets. Each training set comprises a plurality of different training video items that each include a predefined visual augmentation. Based on the generation of the embedding of the input video item, the target visual augmentation is mapped to an augmentation identifier.