Semi-Automatic Visual Data Annotation System
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Solution Overview
Problem
The challenge in deep learning is the time-consuming and labor-intensive process of generating high-quality, precisely annotated data, which is essential for supervised/semi-supervised learning methods, as manual annotation can take months or years to reach an appropriate level.
Innovation Solution
A semi-automatic framework for fast visual data annotation that uses an automatic detector to initialize annotations, followed by sampling and refinement, where users manually correct annotations in select frames and interpolate for non-selected frames, utilizing a graphical user interface with limited operations like the spacebar and mouse buttons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation is used as the gold standard, then annotation quality is improved, but annotation time increases significantly
Solution Approach 1:
The system performs preliminary automatic detection and annotation on video frames before manual review. The automatic detector pre-identifies subjects and joints, creating initial annotations that reduce the manual workload from scratch annotation to verification and correction only.
Solution Approach 2:
Instead of requiring complete manual annotation of all frames, the system applies partial manual action only to sampled frames where automatic detection confidence is lower or where manual correction is most beneficial. The majority of frames rely on automatic detection with selective manual oversight.
2Measurement precision
If complete manual annotation of all frames is performed, then annotation accuracy is improved, but productivity decreases
Solution Approach 1:
The annotation process is segmented into automatic detection phase and manual refinement phase. The system divides frames into categories: fully automatic (high confidence), sampled for manual review (low confidence or critical frames), and uses interpolation for intermediate frames. This segmentation allows parallel processing and optimizes the mix of automatic and manual work.
Solution Approach 2:
The system copies annotation data from manually annotated sampled frames to neighboring non-sampled frames through interpolation. This copying approach maintains consistency across the video sequence while avoiding redundant manual annotation of every frame, thereby increasing productivity without significantly compromising accuracy.
3Loss of time
If selective sampling of frames is used, then annotation time is reduced, but measurement precision may worsen
Solution Approach 1:
The system uses feedback from automatic detection confidence scores to determine which frames require manual annotation. Frames with low confidence or high variability are selected for manual review, while high-confidence frames are processed automatically. This feedback-driven sampling ensures that manual effort is concentrated where it is most needed to maintain precision.
Solution Approach 2:
The system performs preliminary automatic detection on all frames before sampling for manual annotation. This preliminary action provides a baseline that guides the sampling process, ensuring that manual review focuses on frames where automatic detection is less reliable, thereby maintaining overall precision while reducing total annotation time.
4Productivity
If automatic detection is used alone, then productivity is improved, but manufacturing precision worsens
Solution Approach 1:
The system merges automatic detection results with manual annotation through a hybrid workflow. Automatic detection handles the majority of frames for high productivity, while manual annotation corrects and refines the results on sampled frames. The combination leverages the speed of automatic detection and the precision of manual annotation to achieve both high productivity and acceptable accuracy.
Data Source
AI summary
Fast visual data annotation includes automatic detection using an automatic detector to detect subjects and joints in video frames. Then, annotation with sampling is performed, including determining when a frame is a sample (e.g., based on comparison of frames). Replay and refinement is utilized where user is involved with manually annotating subjects and/or joints in only select video frames.