Multimodal AI Training Dataset Annotation for Real-World Scene Understanding
Image
Object DetectionWorked on multimodal AI training datasets focused on improving real-world scene understanding for machine learning systems. The project involved labeling images by identifying objects, environments, and contextual relationships between elements (for example: distinguishing foreground vs background objects and tagging scene context such as “urban street,” “indoor workspace,” or “public transport setting”). In addition, I performed text classification tasks such as intent detection and sentiment labeling, ensuring accurate categorization based on context rather than keywords alone. I also worked on simple audio transcription labeling, identifying spoken content and marking key segments for training voice recognition models. Maintained high accuracy by following strict annotation guidelines, ensuring consistency across large datasets, and reviewing labels to reduce errors and improve model training quality.