Lee R.

Key Skills

Software

Label Studio

CVAT

Labelbox

SuperAnnotate

Top Subject Matter

Stock Data Annotation

Video Subtitle and Watermark Annotation

Game Character and VFX Segmentation

Top Data Types

Document

Video

Image

Text

Top Task Types

Segmentation

Tracking

Classification

Emotion Recognition

Freelancer Overview

1. Stock Data Range Annotation, Stock Price Pattern Annotation (including profitable and loss patterns) 股票数据区间标注、股票走势形态标注（包括盈利和亏损形态） 2. Video Subtitle Annotation, Video Dynamic Watermark Tracking Annotation (watermarks move and morph into entirely different forms) 视频字幕标注、视频动态水印跟踪标注（水印移动并变为完全不同的形式） 3. Game Character and Special Effects Segmentation Annotation (distinguishing characters from variable VFX during action) 游戏角色与特效分隔标注（在动作中区分角色和可变特效区域） 4. Scene Annotation (identifying different entities and attachments within a scene) 场景标注（区分场景中的不同主体与附加物） 5. Social Media Emotion Annotation (labeling user emotions in chats, forums, and comments) 社交媒体情绪标注（标注对话、论坛、评论中的用户情绪）

Labeling Experience

Social Media Emotion Annotation

SuperAnnotateTextEmotion Recognition

Social media emotion annotation required reviewing messages, forum posts, and comments to identify user emotions expressed in text. Each annotation drew on both language cues and context for accurate labeling. Results provided high-quality datasets for sentiment analysis and emotion-detection AI systems. • Labeled examples as positive, negative, or neutral emotions. • Used contextual analysis for ambiguous or sarcastic language. • Cross-checked label consistency with guideline documentation. • Supported iterative model improvement cycles.

Not specified

Scene Annotation (Entity and Attachment Identification)

LabelboxImageClassification

Scene annotation involved identifying different entities and their attachments within complex image scenarios. Annotators classified individual components, ensuring each was logged with relevant properties. The output labels supported object recognition and classification AI workflows. • Classified distinct objects and their attachments within scenes. • Maintained annotation consistency across similar scenarios. • Flagged ambiguous entities for team review. • Assisted in model evaluation and calibration processes.

Not specified

Game Character and Special Effects Segmentation Annotation

CVATImageSegmentation

This annotation task focused on distinguishing game characters from variable special effects during intense in-game actions. The objective was to segment visual data into relevant foreground and effects layers. Precise labeling was critical for subsequent model-based automated segmentation. • Separated player characters from dynamic special effects. • Utilized frame-by-frame analysis for intricate scenes. • Integrated feedback from the quality assurance team. • Contributed to training data for computer vision models.

Not specified

Video Subtitle and Dynamic Watermark Tracking Annotation

Label StudioVideoTracking

In this experience, video subtitle and watermark changes were annotated frame by frame. The work included tracking subtitles for accuracy and following the dynamic movement of watermarks as they morphed throughout the footage. Annotations contributed to training models in both overlay detection and subtitle recognition. • Marked the position and text of subtitles at each timestamp. • Tracked watermarks as they transformed across video clips. • Coordinated annotation consistency across video batches. • Annotated both visible and semi-transparent overlays.

Not specified

Stock Data Range and Pattern Annotation

DocumentSegmentation

This project involved annotating ranges within stock data and labeling price patterns, including both profitable and loss scenarios. The annotation tasks required a detailed understanding of financial time series and technical analysis indicators. Consistency and precision in label boundaries were essential for robust AI model training. • Labeled distinct periods and trends in historical stock charts. • Identified and annotated patterns indicating gains or losses. • Collaborated with team members for segment validation. • Ensured high-quality, accurately delimited data for model use.

Not specified

Education

Work History