Image Patch Embeddings for Precise Positional Retrieval
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Solution Overview
Problem
Conventional image retrieval systems using language models suffer from low precision due to conflating multiple visual features, lacking positional awareness, and difficulty processing complex queries, often requiring extensive training and resources.
Innovation Solution
Segment images into portions, generate embeddings for these portions, and use user feedback to refine queries, enhancing precision by filtering results based on positional information and user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If additional training with custom datasets is performed to improve language models, then precision is improved, but time and computing resources are consumed
Solution Approach 1:
The patent segments images into multiple portions (e.g., patches, tiles, or regions) and processes each portion independently to generate embeddings. This segmentation allows the system to handle complex queries by analyzing specific regions without requiring extensive retraining, thereby improving precision without significant time investment.
Solution Approach 2:
The system performs preliminary processing by segmenting images and generating embeddings in advance, storing these pre-processed portions in a database. When queries arrive, the system can quickly retrieve and match pre-computed embeddings rather than performing complex real-time analysis or retraining, thus reducing time loss while maintaining high precision.
2Measurement precision
If additional training with custom datasets is performed to improve language models, then precision is improved, but computing resources are consumed
Solution Approach 1:
By dividing images into smaller portions and processing them independently, the system reduces the computational burden compared to processing entire images or performing complex model training. This segmentation approach allows for efficient resource utilization while maintaining the ability to handle complex queries with high precision.
Solution Approach 2:
The system creates and stores embeddings as simplified representations (copies) of image portions in a database. These pre-computed embeddings serve as efficient search indices, allowing quick retrieval and matching without requiring intensive real-time computing resources or additional training, thus reducing overall computing resource consumption.
3Device complexity
If language models process queries without positional awareness, then processing is simplified, but precision for location-based queries deteriorates
Solution Approach 1:
The patent segments images into multiple portions and associates each portion with positional information (e.g., coordinates, spatial relationships). This segmentation enables the system to maintain simple overall processing while incorporating positional awareness at the portion level, thereby improving precision for location-based queries without significantly increasing model complexity.
Solution Approach 2:
The system applies local quality by attaching positional metadata and context-specific embeddings to each image portion rather than treating the entire image uniformly. This allows the system to preserve spatial relationships and provide accurate location-based retrieval while keeping the core processing architecture relatively simple and efficient.
4Device complexity
If language models conflate multiple visual features for a same description, then processing is simplified, but precision deteriorates
Solution Approach 1:
The patent segments visual features by processing different portions of images separately and generating distinct embeddings for each portion. This segmentation allows the system to distinguish between multiple visual features (e.g., objects, backgrounds, foreground elements) without conflating them, thereby improving precision while maintaining manageable processing complexity through modular portion-based analysis.
Solution Approach 2:
The system applies local quality by generating and storing separate embeddings for different portions of images, each capturing the specific visual characteristics of that region. This approach enables the system to differentiate between multiple visual features accurately while keeping the processing architecture organized and manageable, avoiding the confusion that arises from conflating all features into a single representation.
Data Source
AI summary
In various examples, techniques for improving image retrieval precision for machine learning systems and applications is described herein. Systems and methods described herein may segment images into various portions (e.g., patches, tiles, areas, regions, etc.) and then use data associated with the portions to perform a search. For instance, after segmenting the images into the portions, one or more models may process the images in order to generate the data for the portions, such as data representing embeddings, identifiers, locations, and/or any other information. This data may then be used to identify at least a set of images when performing a search for a query. Additionally, systems and methods described herein may perform improved searches using compositable queries and/or user feedback.