Attention-Based Image Representations for Diverse Object Search
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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 result in 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 through a user interface for targeted searches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
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 contain redundant examples that do not improve model accuracy
Solution Approach 1:
The patent segments an image into multiple patches and generates separate representations for each patch using a transformer model. This segmentation allows the system to capture local features and contextual relationships that traditional global image representations miss, thereby improving the diversity and quality of retrieved training images without overwhelming complexity through modular processing
Solution Approach 2:
The patent transitions from traditional 2D image space to a higher-dimensional feature space by generating patch representations with attention mechanisms. This dimensional transformation enables the system to capture complex relationships and contextual information, allowing for more accurate and diverse image retrieval that directly addresses the accuracy improvement goal
2Measurement precision
If a diverse set of training images with objects in different contexts is used, then object detection accuracy improves significantly, but the complexity of curating and searching for such diverse images increases substantially
Solution Approach 1:
The patent performs preliminary action by pre-computing and storing patch representations and attention maps for images in the database before retrieval is needed. This preprocessing step enables fast similarity searches and diverse image retrieval without time-consuming computations during the actual training data assembly process, directly reducing the time loss
Solution Approach 2:
The patent creates multiple representations (class token, attention-based patch, object-based) of the same image, effectively creating copies in different feature spaces. These multiple copies enable the system to retrieve diverse training images efficiently by querying different representation types, avoiding the need to manually curate diverse datasets and significantly reducing curation time
3Reliability
If multiple types of image representations (class token, attention-based patch, object-based) are generated using transformer models, then the training dataset diversity and model accuracy improve, but the computational resources and processing time required increase
Solution Approach 1:
The patent applies local quality by generating different types of representations (class token, attention-based patch, object-based) for different regions and aspects of the image. Each representation type focuses on specific local features or global context, allowing the system to achieve high classification reliability through targeted feature extraction rather than uniformly processing the entire image with maximum computational resources
Solution Approach 2:
The transformer model in the patent serves multiple functions by generating different representation types from the same input image. This multi-functionality allows a single computational pass to produce class tokens for overall classification, attention-based patches for local feature analysis, and object-based representations for specific object detection, reducing redundant computations and energy consumption while maintaining high reliability
Data Source
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.