Core Annotation & QA Expertise (30+ Projects)
🤖 Robotics, VLA & Egocentric Tracking
- Hand & Bimanual Tracking: Point labeling on fingers/hands, 21-keypoint pose skeletons, and timestamped hand gesture tracking.
- Human Pose & Movement: Body posture annotation, body landmarks, gym exercise video tracking, and full-body/limb bounding boxes.
- Spatial & Object Interaction: Fine-grained object tracking and multi-class bounding box annotation (full body, head, face, upper body).
👤 Facial, Biometric & Fine-Grained Segmentation
- Face & Biometric QA: Facial landmarking, face mask detection, and edge cases like eye occlusion behind glasses with glint and reflection.
- Pixel-Level Segmentation: Face segmentation, object segmentation, and semantic/instance image and video segmentation.
🎬 Video Annotation & Temporal Tracking
- Action & Sports Tracking: Action segmentation in sports videos and complex video object tracking across continuous frames.
- Multi-Frame QA: Ensuring temporal consistency across continuous video streams and frame-by-frame object motion.
🎙️ Multimodal, Audio-Visual & Language
- Audio-Visual Alignment: Conversation tracking, dialogue description, and instrument/object sound source tracking.
- Scene & Image Captioning: Indoor/outdoor image descriptions, scene summaries in Hindi, and rich context labeling.
- GenAI Evaluation: Human evaluation and safety/quality scoring for AI-generated images and videos.
Where I've applied this
- Image annotation
- Video annotation
- Object detection
- Segmentation
- Tracking
- QA review
- Dataset validation
- Guideline compliance
- Robotics AI annotation
- Vision-Language-Action annotation
End-to-End System Architecture
Visualizing dataset lifecycles, human-in-the-loop workflows, VLA grounding, and inter-annotator metrics. Click any diagram to expand.
VLA Pipeline Architecture
Linking natural language instructions, visual perception, cross-modal alignment, and action token execution.
Robotics Data Lifecycle
Complete lifecycle from raw sensor ingestion, curation, multi-layer QA, versioning, to active learning feedback loops.
Annotation Pipeline & QA Levels
Structural pipeline covering data preparation, multi-tier QA levels (L1-L4), and platform integration.
Human-in-the-Loop Workflow
Iterative refinement combining AI pre-annotation, human review, automated validation, and continuous model feedback.
Inter-Annotator Agreement
Statistical tracking of Cohen's Kappa, IoU metrics, temporal segment agreement, and edge-case flagging.
QA Coverage & Consensus
Dashboard tracking consensus scores, dual-review distributions, reviewer performance, and severity-graded edge cases.
Fine-Grained Annotation Deliverables
Annotated sequences showcasing multi-modal tracking, pose skeletons, temporal actions, and semantic segmentation.
Robot Opening Refrigerator
Pose skeletons, interaction points, semantic segmentation, and temporal action boundaries across an 8-stage interaction sequence.
Video Annotation – Object & Activity Tracking
Trajectory tracking, 3D end-effector coordinate logging, confidence scoring, and frame-by-frame temporal phase progression.
Multi-Item Trajectory Analysis
End-to-end top-view tracking trajectories across multiple object classes, semantic masks, and timeline breakdown.
Annotation platforms & Frameworks
Industry-standard labeling tools and quality management environments.
Get the full portfolio PDF
A detailed walkthrough of my annotation process, QA framework, and sample workflows — the long-form version of everything on this page.
Let's talk
Open to remote AI Data Annotation, QA, and Robotics AI opportunities.
Connect on LinkedIn
bhatnagarshivam285@gmail.com
Location
India
Availability
Open to remote roles