Configurable Detector for Dynamic Feature Annotation
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Solution Overview
Problem
Existing image processing systems face challenges in accurately identifying features in images due to the need for large datasets with multiple instances of features, which restricts feature identification and the ability of detectors to self-train or dynamically evolve, leading to degraded quality and user frustration.
Innovation Solution
A system that uses a configurable detector to identify features in images, selectively obtains feedback from users based on accuracy metrics and costs, generates revised tags when necessary, and updates labeled data to retrain the detector, allowing for dynamic adaptation and improvement of feature identification even with limited initial data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a detector is trained using a dataset with multiple instances of features, then the accuracy of feature identification is improved, but it becomes challenging to obtain multiple instances of arbitrary features a priori
Solution Approach 1:
The system performs preliminary action by using a pre-trained detector to identify features in images and generate candidate tags before actual training data is available. This allows the system to start operating with limited data and progressively improve through feedback-driven learning.
Solution Approach 2:
The system implements feedback by selectively obtaining feedback about candidate tags from users or external sources, using this feedback to update the labeled training data, and retraining the detector to improve its accuracy over time. This creates a continuous improvement loop that resolves the data scarcity problem.
2Reliability
If feature identification is restricted to certain types of features with large training datasets, then the quality of identified features is maintained, but the ability of the system to self-train or dynamically evolve is restricted
Solution Approach 1:
The system applies dynamics by making the detector configurable and adaptable. The detector can be dynamically retrained with new labeled data generated from feedback, allowing it to evolve and adapt to new feature types and conditions rather than being static and restricted to pre-defined categories.
Solution Approach 2:
The system implements self-service by automatically generating candidate tags, obtaining feedback, updating training data, and retraining the detector without requiring manual intervention for each feature type. This enables the system to self-train and expand its capabilities autonomously.
3Measurement precision
If feedback is obtained from multiple individuals to improve tag accuracy, then the quality of feature annotation is improved, but the cost and time required increases
Solution Approach 1:
The system applies partial action by selectively obtaining feedback only when necessary, rather than requesting feedback from all individuals for every candidate tag. The feedback request is triggered based on conditions such as low confidence scores or specific accuracy thresholds, reducing unnecessary feedback requests and associated costs.
Solution Approach 2:
The system changes parameters by adjusting the feedback threshold and selection criteria based on the specific context, feature type, and detector confidence levels. This dynamic parameter adjustment optimizes the balance between annotation quality and feedback cost, requesting feedback only when it will provide the most value.
Data Source
AI summary
A system may use a configurable detected to identify a feature in a received image and an associated candidate tag based on user-defined items of interest, and to determine an associated accuracy metric. Moreover, based on the accuracy metric, costs of requesting the feedback from one or more individuals and a feedback threshold, the system may use a scheduler to selectively obtain feedback, having a feedback accuracy, about the candidate tag from the one or more individuals. Then, the system may generate a revised tag based on the feedback when the feedback indicates the candidate tag is incorrect. Next, the system presents a result with the feature and the candidate tag or the revised tag to another electronic device. Furthermore, based on a quality metric, the system may update labeled data that are to be used to retrain the configurable detector.