AR Effect Description Model Fine-Tuning for Background Separation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing interaction systems face challenges in automating the generation of accurate and relevant descriptions for augmented reality (AR) effects due to difficulties in distinguishing between AR effects and background content, leading to inefficient search, indexing, and classification of AR effects.
Innovation Solution
Utilizing a visual-semantic machine learning model, specifically through parameter-efficient fine-tuning techniques like LoRA, to generate precise descriptions of AR effects by focusing on the transition caused by the effects, thereby separating AR effects from background content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a visual-semantic machine learning model is used to generate AR effect descriptions, then description accuracy is improved, but computational resource consumption increases
Solution Approach 1:
The patent segments the machine learning model into a pre-trained base model and task-specific adapter modules (LoRA). The base model retains general visual-semantic understanding capabilities, while the adapter modules are specifically trained to distinguish AR effects from background content. This segmentation allows the system to achieve high description accuracy for AR effects without requiring the entire model to be retrained, thereby reducing computational resource consumption during fine-tuning while maintaining measurement precision.
2Productivity
If parameter-efficient fine-tuning techniques like LoRA are used, then training efficiency is improved, but model adaptability may be reduced
Solution Approach 1:
The patent introduces adapter modules as intermediary components between the pre-trained base model and the specific AR effect description task. These adapter modules act as a bridge, allowing the model to adapt to the specific task of distinguishing AR effects from background content without modifying the entire base model. This intermediary approach enables efficient training while preserving the model's general adaptability, as the adapter modules can be independently trained and adjusted for different AR effect types.
3Reliability
If the model focuses on distinguishing AR effects from background content, then search relevance is improved, but complexity of the model increases
Solution Approach 1:
The patent applies local quality by enhancing only the specific parts of the model that are relevant to distinguishing AR effects from background content, rather than increasing the complexity of the entire model. The adapter modules are designed to process and highlight AR effect-specific features locally, while the base model continues to handle general image understanding. This localized enhancement improves search relevance for AR effects without proportionally increasing overall model complexity.
Data Source
AI summary
A second input image is generated by applying a target augmented reality (AR) effect to a first input image. The first input image and the second input image are provided to a first visual-semantic machine learning model to obtain output describing at least one feature of the target AR effect. The first visual-semantic machine learning model is fine-tuned from a second visual-semantic machine learning model by using training samples. Each training sample comprises a first training image, a second training image, and a training description of a given AR effect. The second training image is generated by applying the given AR effect to the first training image. A description of the target AR effect is selected based on the output of the visual-semantic machine learning model. The description of the target AR effect is stored in association with an identifier of the target AR effect.