data label, auditor, disputor
Project Scope (What the project was about) Worked on large-scale computer vision data annotation projects focused on training and improving AI models for real-world applications such as: Human activity recognition Object detection and tracking Scene understanding in images and videos Human-object interaction modeling The project involved preparing high-quality labeled datasets used to train machine learning models for automation, surveillance, and intelligent systems. Project Type Computer Vision (CV) / AI Training Dataset Development Supervised Learning Dataset Preparation Image & Video Annotation Pipelines Human-in-the-loop AI systems Specific Data Labeling Tasks Performed Image Annotation Bounding box annotation for object detection Semantic and instance segmentation Image classification and tagging Multi-object labeling in complex scenes Video Annotation Frame-by-frame labeling of actions and objects Temporal segmentation of activities Tracking objects across frames Annotating human-object interactions Behavioral / Action Annotation Labeling sequences like: “pick up object" “place item” “open/close” Segmenting actions into structured steps based on intent Data Structuring Applying Tier-based annotation frameworks Splitting actions into meaningful segments (single-intent rule) Maintaining consistency across large datasets Quality Measures Adhered To: Accuracy & Precision Maintained 98% QA accuracy across annotation tasks Ensured precise bounding boxes, segmentation masks, and labels Annotation Guidelines Compliance Strictly followed annotation playbooks and task-specific rules Applied consistent labeling logic across all datasets Consistency Control Ensured uniform labeling across similar objects and actions Avoided ambiguity in labeling decisions QA & Review Process Participated in multi-stage QA workflows: Self-review Peer review Final QA validation Edge Case Handling Properly labeled complex or unclear scenarios Flagged ambiguous data for review instead of guessing Efficiency & Throughput Delivered high-volume annotation tasks within deadlines.