AI Data Annotation
Performed high-fidelity Egocentric (First-Person) Action Annotation and video segmentation to develop ground truth datasets for AI computer vision and robotic object manipulation models. Maintained a rigorous, high-volume production throughput of 30 comprehensive annotation tasks per day to meet strict target milestones. Handled detailed micro-tasking by breaking down complex human behaviors into atomic, low-level mechanical verb-noun pairs (e.g., mapping precise tool interactions with cylinders, canisters, and syringes) while entirely stripping away human intent or outcome-biased language. Enforced stringent quality control measures to adhere to platform-specific guidelines, including the precise application of resting-state boundary rules for object manipulation and eliminating functional audit flags to ensure flawless data uniformity.