Caption Refinement Project for Video Content
This project focused on improving captioning accuracy for video content, aimed at enhancing the understanding of model multimodal outputs. It involved data labeling and annotation tasks, including text summarization, object and action recognition, and emotion recognition. We employed techniques such as identifying errors related to relative position, trajectory, and celebrity recognition. Our goal was to ensure captions accurately reflected video content, focusing on spatial relationships, logical reasoning, and textual analysis to produce coherent and engaging content summaries. The project emphasized correcting grammar errors, handling subtitled inconsistencies, and ensuring captions adhered to the content's primary focus while maintaining consistency in theme and context.