Dynamic Annotation Algorithm Parameter Adjustment
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Solution Overview
Problem
Current data annotation in the artificial intelligence industry is labor-intensive and inefficient, relying on manual processes despite prior attempts to improve efficiency through recognition algorithms.
Innovation Solution
A method and apparatus that acquire an original annotation algorithm, extract an anchor point, determine a target anchor point based on annotation requirements, and modify implementation parameters to generate a target annotation algorithm for processing data, thereby improving annotation efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual data annotation is used, then annotation quality can be ensured, but annotation efficiency is low and the process is labor-intensive
Solution Approach 1:
The annotation algorithm is segmented into multiple functional modules including data acquisition module, annotation execution module, and result output module. Each module handles specific tasks independently, allowing for automated processing while maintaining quality control at each stage of the annotation pipeline
Solution Approach 2:
Manual mechanical annotation operations are replaced with an automated computer-based annotation system that executes predefined algorithms. The system automatically processes data, applies annotation rules, and generates results without human manual intervention, thereby improving efficiency while maintaining consistency
2Adaptability or versatility
If a fixed annotation algorithm is used, then the algorithm is simple to implement, but it lacks adaptability to different annotation requirements
Solution Approach 1:
The annotation algorithm transitions from a static fixed configuration to a dynamic adaptable system. The system can dynamically adjust annotation parameters, select different annotation strategies, and modify processing logic based on the specific requirements of different data types and annotation tasks, enabling versatility without requiring completely different algorithms for each scenario
Solution Approach 2:
The annotation system utilizes configurable parameters that can be adjusted according to different annotation requirements. By changing parameters such as annotation granularity, data formats, and processing rules, the same core algorithm can adapt to various annotation scenarios, maintaining simplicity while achieving versatility
3Productivity
If recognition algorithms are used for preprocessing, then some efficiency improvement is achieved, but the overall annotation process remains labor-intensive
Solution Approach 1:
The system merges multiple functions including data preprocessing, annotation execution, quality validation, and result output into a single integrated automated pipeline. This consolidation eliminates the need for separate manual intervention steps, achieving complete automation from raw data to annotated results while maintaining high efficiency
Data Source
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AI summary
The present disclosure provides a method and apparatus for annotating data, an electronic device, a computer readable storage medium and a computer program product, and relates to the field of artificial intelligence technology such as data annotation and deep learning. A specific implementation of the method comprises: acquiring an original annotation algorithm corresponding to to-be-annotated data, and then extracting, from the original annotation algorithm, an anchor point used to mark a modifiable part of a code corresponding to a preset function in a code segment of the original annotation algorithm; acquiring an annotation requirement corresponding to the to-be-annotated data, and determining a target anchor point corresponding to the annotation requirement; modifying an implementation parameter of the target anchor point based on the annotation requirement, to generate a target annotation algorithm; and finally processing the to-be-annotated data using the target annotation algorithm, to obtain an annotation result.