System and method for semi-automated dataset annotation using similarity based clustering and in-context learning for segmentation
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Solution Overview
Problem
Existing annotation methods for large and diverse datasets are time-consuming, computationally expensive, and prone to inaccuracies due to reliance on manual or semi-automated processes, particularly in segmentation tasks.
Innovation Solution
A semi-automated dataset annotation method using similarity-based clustering and in-context learning, where images are clustered based on similarity and a subset is manually annotated, allowing computer vision models to generate accurate annotations for the remainder.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation methods are used, then annotation accuracy can be maintained, but the time consumption and labor cost increase significantly
Solution Approach 1:
The patent segments the annotation process into multiple stages: initial manual annotation of a subset, automated model training, iterative refinement cycles, and final validation. This segmentation allows the system to leverage both manual precision and automated efficiency at different phases, reducing overall time consumption while maintaining accuracy.
Solution Approach 2:
The patent performs preliminary manual annotation on a representative subset of data before automated processing. This preliminary action creates a foundation of high-quality labeled data that guides subsequent automated annotation, ensuring accuracy is established early while enabling faster processing of remaining data.
2Productivity
If fully automated annotation processes are used, then productivity increases, but computational cost and resource requirements become excessive
Solution Approach 1:
The patent applies partial automation by manually annotating only a subset of data (e.g., 10-30%) rather than the entire dataset. This partial manual action provides sufficient training signals for the automated model, achieving high productivity for the majority of data while keeping computational costs manageable through targeted rather than exhaustive processing.
Solution Approach 2:
The system implements self-service through automated model training and iterative refinement, where the annotation model improves autonomously through multiple training cycles using generated predictions as pseudo-labels. This self-improving mechanism reduces the need for continuous manual intervention and expensive re-annotation, maintaining high productivity with controlled computational expenditure.
3Loss of time
If semi-automated methods are used, then time consumption is reduced, but annotation accuracy and robustness deteriorate
Solution Approach 1:
The patent implements multi-stage feedback mechanisms where automated predictions are continuously evaluated against manual annotations, and model parameters are adjusted based on performance metrics. This feedback loop ensures that annotation robustness is maintained and improved over time, counteracting the potential deterioration from reduced manual involvement.
Solution Approach 2:
The system dynamically adapts the annotation strategy based on data characteristics, model performance, and resource availability. Annotation thresholds, sampling rates, and refinement frequencies are adjusted dynamically to maintain robustness across different dataset types and annotation stages, preventing accuracy deterioration while preserving time efficiency.
4Productivity
If automated annotation is applied to diverse datasets, then productivity increases, but measurement precision decreases due to heterogeneity
Solution Approach 1:
The patent segments diverse datasets into homogeneous subgroups or clusters based on visual characteristics, object types, or scene categories. This segmentation allows the annotation model to specialize in specific domains within the diverse dataset, maintaining high precision for each subgroup while achieving overall high productivity across the entire heterogeneous collection.
Data Source
AI summary
A method receiving a dataset including a plurality of images, executing an embedding model using the plurality of images to generate a plurality of image embeddings, clustering the plurality of image embeddings into a plurality of clusters based on similarities between the plurality of image embeddings, determining a density for each cluster based on an aggregate similarity of image embeddings of the cluster, based on the density for each of the plurality of clusters satisfying a criterion, for each cluster: receiving a first set of annotations for one or more pixels of each of a first subset of the images included in the cluster, inputting the annotated first subset of images into a vision transformer with a remaining unannotated subset of images not included in the annotated first subset, and generating a second plurality of annotations for the remaining unannotated subset of images not included in the annotated first subset.