Attention-Based Granular Neighbor Search for Diverse Object Training
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Solution Overview
Problem
Existing object detection models in agricultural equipment face inaccuracies due to insufficiently diverse training datasets, leading to misclassifications such as mistaking a telephone pole's shadow for a person, which can cause operational failures or injuries.
Innovation Solution
Generate multiple types of image representations using a transformer-based model, including class token, attention-based patch, and object-based representations, to enrich the training dataset with diverse examples, and utilize these representations to create synthetic images and enhance the object detection model's accuracy through a user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional image similarity search methods (Euclidean distance, cosine similarity) are used to gather training images, then the search process is simple and fast, but the retrieved images lack diversity and lead to insufficient training data variety
Solution Approach 1:
The patent segments an image into multiple patches and generates different types of representations for each patch (class token representations, attention-based representations, object-based representations). This segmentation allows the system to capture diverse features from different parts of the image, thereby increasing training image diversity without requiring complex external data sources.
Solution Approach 2:
The patent transforms a single image into multiple dimensional representations by creating class token representations, attention-based patch representations, and object-based representations. This multi-dimensional transformation enriches the training data variety by presenting the same image from multiple feature perspectives, solving the diversity problem without increasing data quantity.
2Reliability
If multiple types of image representations are generated using transformer-based models, then training data diversity is enhanced, but the processing time and computational resources increase
Solution Approach 1:
The patent generates and stores multiple types of image representations (class token, attention-based, object-based) in advance during a preprocessing phase. These pre-generated representations are stored in a database for quick retrieval during training, eliminating the need to generate them repeatedly during the actual training process, thus reducing processing time while maintaining high accuracy.
Solution Approach 2:
The patent creates multiple copies of image representations from a single source image, storing different types of representations (class token, attention-based, object-based) separately. These copies can be independently retrieved and used during training, reducing the computational burden of generating representations on-the-fly while ensuring high detection accuracy through diverse feature coverage.
3Reliability
If a diverse set of training images with objects in different contexts is used, then object detection accuracy improves, but the data collection and processing complexity increases
Solution Approach 1:
The patent creates a universal image representation generation system that produces multiple types of representations (class token, attention-based, object-based) from a single image input. This multi-functional system can serve different training needs simultaneously, providing diverse contextual information without requiring separate data collection processes for each type of diversity, thus improving accuracy while managing complexity.
Data Source
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AI summary
A method may include presenting a user interface, the user interface including a set of image similarity search options; receiving a selected image similarity search option of the set of the image similarity search options, the selected image similarity search option associated with a type of image representation; accessing an input query image file; generating an image representation of the input query image file according to the selected image similarity search option using a transformer model; querying an image representations database for image representations of a type that matches the type of image representation associated with the selected image similarity search option; filtering image representations resulting from the querying to a result set of image representations; and outputting a set of image files associated with the result set of image representations.