Assisted Image Annotation Using ML Pre-Annotation
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Solution Overview
Problem
Human annotation for machine learning model training is time-consuming and costly due to the need for large datasets, which can be improved by leveraging machine-assisted annotation techniques.
Innovation Solution
A system that uses initial object prediction information from machine learning models, displayed to human annotators via a user interface, allowing for adjustments and improvements, thereby enhancing annotation speed and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human annotators manually annotate objects in images from scratch, then annotation accuracy can be maintained at human-level standards, but annotation speed and productivity are significantly reduced
Solution Approach 1:
The system performs preliminary annotation actions by generating initial bounding boxes and object predictions using machine learning models before human annotators review them. This preliminary action reduces the workload on human annotators while maintaining accuracy, as they only need to refine rather than create annotations from scratch.
Solution Approach 2:
The system introduces an intermediary machine learning model that generates initial annotations which serve as a bridge between automated processing and human review. This intermediary provides a starting point that improves productivity while human annotators maintain quality control, resolving the contradiction between speed and accuracy.
2Reliability
If large numbers of images are annotated to train machine learning models, then model recognition capabilities improve, but the time and cost required for annotation increase significantly
Solution Approach 1:
The system performs preliminary annotation using machine learning models to generate initial bounding boxes and predictions, which are then refined by human annotators. This approach enables rapid generation of large datasets for training while maintaining quality, reducing both time and cost compared to manual annotation from scratch.
Solution Approach 2:
The system enables partial self-service annotation where machine learning models automatically generate initial annotations that can be used directly or refined by humans. This self-service capability allows the system to handle large volumes of images efficiently while maintaining adequate quality for model training.
3Productivity
If machine learning models are used for automated annotation, then annotation speed increases, but annotation accuracy and reliability decrease compared to human annotation
Solution Approach 1:
The system merges the strengths of both machine learning models and human annotators by combining automated initial annotation with human review and refinement. This hybrid approach achieves both high speed from automation and high accuracy from human expertise, resolving the contradiction between productivity and precision.
Solution Approach 2:
The system implements feedback loops where human annotators review and correct machine-generated annotations, and these corrected annotations are used to retrain and improve the machine learning models. This feedback mechanism ensures continuous improvement of both speed and accuracy over time.
Data Source
AI summary
Image annotation includes: accessing initial object prediction information associated with an image, wherein the initial object prediction information includes a plurality of initial predictions associated with a plurality of objects in the image, including bounding box information associated with the plurality of objects; presenting the image and at least a portion of the initial object prediction information to be displayed; receiving adjusted object prediction information pertaining to at least some of the plurality of objects, wherein the adjusted object prediction information is obtained from a user input made via a user interface configured for a user to make annotation adjustments to at least some of the initial object prediction information; and outputting updated object prediction information, wherein the updated object prediction information is based at least in part on the adjusted object prediction information.