Image Annotation & Visual Content Summarization for AI Training
In this project, I worked on large-scale image annotation and visual content summarization with a focus on human activities and real-world environments to support the training of computer vision and multimodal AI models. My primary responsibility was to analyze images and generate clear, concise summaries describing visible elements such as people, objects, actions, settings, and contextual relationships. The dataset included a wide range of scenarios such as daily life activities, workplace environments, street scenes, and social interactions. This required not only identifying objects but also interpreting behavior, intent, and context to produce meaningful descriptions. I maintained strict adherence to annotation guidelines to ensure consistency, accuracy, and high-quality outputs aligned with model training requirements.