Visual Augmentation Embeddings for Accurate Effect Identification
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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 the visual effects of augmentations by minimizing loss between transformed and original video 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 created, but they fail to accurately capture the effects of augmentations leading to limited utility in identifying, comparing, or ranking them
Solution Approach 1:
The patent segments the visual augmentation representation task into two distinct components: (1) generating embeddings that capture the visual effects of augmentations applied to content, and (2) separately generating embeddings that represent the augmentation effects themselves. This segmentation allows the system to focus on capturing augmentation-specific features rather than mixing them with content features, thereby improving measurement precision and reliability of augmentation identification and comparison.
Solution Approach 2:
The patent extracts the visual augmentation effects from the overall video representation by using a trained machine learning model to generate separate embeddings that specifically represent augmentation effects. This extraction process isolates the augmentation characteristics from the underlying content, enabling accurate identification, comparison, and ranking of augmentations without interference from content variability.
2Measurement precision
If a machine learning model is trained to generate embeddings capturing visual augmentation effects, then accurate representation is achieved, but computational resources and training time increase
Solution Approach 1:
The patent applies preliminary action by pre-training the machine learning model on a large dataset of video pairs with various visual augmentations before deployment. This preliminary training phase establishes the model's ability to accurately capture augmentation effects, and once trained, the model can efficiently generate embeddings without requiring extensive computational resources during actual augmentation representation tasks.
Solution Approach 2:
The patent uses copying by generating multiple video pairs with the same augmentation applied to different content, and training the model to produce consistent embeddings for the same augmentation across different copies. This approach allows the model to learn robust augmentation representations that generalize well, improving accuracy while maintaining computational efficiency through reusable training patterns.
3Reliability
If embeddings are generated for each visual augmentation to enable accurate identification and comparison, then the utility of augmentations improves, but the complexity of the system increases
Solution Approach 1:
The patent implements universality by designing a single machine learning model that serves multiple functions: generating embeddings for visual augmentation effects, enabling identification of augmentations, supporting comparison between augmentations, and facilitating ranking. This multi-functional approach improves reliability across multiple tasks while avoiding the complexity of implementing separate systems for each function.
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.