Prelabeling Semantic Segmentation Using Multi-Scale ML Predictions
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Manual labeling of data for machine learning models is time-consuming, labor-intensive, and prone to variations in accuracy, affecting the performance of machine learning models due to inconsistent human interpretation and labeling quality.
Innovation Solution
A technique that generates a multi-scale representation of an image, applies a machine learning model to produce semantic segmentation predictions, and outputs these predictions in a user interface to assist users in confirming or correcting labels, reducing the need for extensive user input and improving labeling efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling is used to tag data with appropriate labels, then labeling accuracy can be controlled through human judgment, but the process becomes time consuming and labor intensive
Solution Approach 1:
The system performs preliminary automated labeling using a machine learning model before human users perform final labeling. The model generates initial labels that users can review and correct, significantly reducing the time users need to spend on labeling while maintaining accuracy through human oversight of the pre-generated labels.
2Productivity
If multiple people are used to generate labels for the same task, then productivity increases, but label quality varies depending on individual interpretation and experience
Solution Approach 1:
The system implements a feedback mechanism where the machine learning model is trained on user corrections to pre-generated labels. User feedback on label accuracy is used to continuously improve the model, creating a closed-loop system that increases both productivity and label consistency over time.
Solution Approach 2:
The machine learning model acts as an intermediary between multiple users and the final labeled dataset. Instead of multiple users directly producing labels independently, the model generates initial labels that all users review and correct, standardizing the labeling process while maintaining high productivity through parallel user involvement.
3Manufacturing precision
If conventional techniques require users to specify labels for all pixels in an image, then complete labeling coverage is achieved, but processing time and resource consumption increase
Solution Approach 1:
Instead of requiring users to label all pixels from scratch, the system applies partial action by having users only review and correct a subset of pixels where the model's pre-generated labels are uncertain or incorrect. This significantly reduces processing time and resource consumption while maintaining complete labeling coverage through the combination of automated and manual labeling.
Data Source
AI summary
One embodiment of the present invention sets forth a technique for performing a labeling task. The technique includes generating a multi-scale representation of an image as input to a machine learning model. The technique also includes performing one or more operations that apply the machine learning model to the multi-scale representation of the image to produce a semantic segmentation comprising predictions of labels for regions of pixels in the image. The technique further includes outputting, in a user interface, the semantic segmentation for use in assisting a user in specifying the labels for the pixels in the image.