Data Annotation
The process of adding structured labels to raw data during AI model training. Common forms include bounding boxes, semantic segmentation, and image classification. Data annotation is one of the most time-intensive and costly core steps in computer vision and machine learning pipelines.
Timeline (last 90 days)
Entry-point contamination is a classic problem in the field of data annotation, referring to bias introduced at the starting point of data generation or collection that is difficult to eliminate later.
When multiple annotators give different judgments, engineering practice typically compresses the results into a single label through aggregation methods such as voting or averaging, or treats the disagreement as noise to be discarded.
Self-collection plus outsourced annotation can best match real-world scenarios, but requires large upfront investment and long cycles, and demands the establishment of strict annotation quality control processes.
Data annotation work is typically paid per piece, time-pressured, and low in unit price, giving annotators a motivation to improve efficiency.
All Facts (4)
Entry-point contamination is a classic problem in the field of data annotation, referring to bias introduced at the starting point of data generation or collection that is difficult to eliminate later.
50%UnverifiedWhen multiple annotators give different judgments, engineering practice typically compresses the results into a single label through aggregation methods such as voting or averaging, or treats the disagreement as noise to be discarded.
50%UnverifiedSelf-collection plus outsourced annotation can best match real-world scenarios, but requires large upfront investment and long cycles, and demands the establishment of strict annotation quality control processes.
50%UnverifiedData annotation work is typically paid per piece, time-pressured, and low in unit price, giving annotators a motivation to improve efficiency.
50%