Method, system, and computer-readable medium for training a captioner model to generate captions for video content by analyzing and predicting cinematic elements

A computing system for training a captioner model to generate captions for video content based on cinematic elements, comprising:

• one or more processors;
• and
• one or more memories, having stored thereon instructions that, when executed, cause the computing system to:
• organize a dataset comprising raw video clips and corresponding metadata, wherein each video clip represents a specific shot varying one cinematic parameter at a time selected from the group consisting of focal length, camera movement, and framing;
• extract frames from the raw video clips at a consistent frame rate and store the frames in a structured format;
• associate each frame with corresponding metadata detailing the cinematic elements present in the frame, wherein the metadata includes information on focal length, camera movement, object distance, and framing style;
• segment the raw video clips into shots and frames, wherein a shot comprises a continuous sequence captured without cuts, and frames are extracted at regular intervals from each shot;
• apply frame-level labels to each frame based on the corresponding metadata, wherein the frame-level labels include focal length used during the shot, camera movement details, object distance from the camera, and framing style;
• aggregate the frame-level labels to generate shot-level labels by calculating average focal length, determining predominant framing style, and smoothing camera movement data across the shot;
• train the captioner model using the frame-level labels, the frames and the aggregated shot-level labels to recognize and predict the cinematic elements in unseen video content;
• iteratively refine the captioner model based on feedback from validation datasets to improve accuracy of cinematic element prediction;
• deploy the trained captioner model to process and label a large video database, wherein the captioner model generates metadata for new video content based on learned cinematic elements;
• and
• post-process the generated labels to ensure consistency and accuracy, including performing outlier detection, confidence scoring, and manual quality control.

A non-transitory computer-readable medium having stored thereon a set of instructions that, when executed, cause a computer to:

• organize a dataset comprising raw video clips and corresponding metadata, wherein each video clip represents a specific shot varying one cinematic parameter at a time selected from the group consisting of focal length, camera movement, and framing;
• extract frames from the raw video clips at a consistent frame rate and store the frames in a structured format;
• associate each frame with corresponding metadata detailing cinematic elements present in the frame, wherein the metadata includes information on focal length, camera movement, object distance, and framing style;
• segment the raw video clips into shots and frames, wherein a shot comprises a continuous sequence captured without cuts, and frames are extracted at regular intervals from each shot;
• apply frame-level labels to each frame based on the corresponding metadata, wherein the frame-level labels include focal length used during the shot, camera movement details, object distance from the camera, and framing style;
• aggregate the frame-level labels to generate shot-level labels by calculating average focal length, determining predominant framing style, and smoothing camera movement data across the shot;
• train a captioner model using the frame-level labels, the frames and the aggregated shot-level labels to recognize and predict the cinematic elements in unseen video content;
• iteratively refine the captioner model based on feedback from validation datasets to improve accuracy of cinematic element prediction;
• deploy the trained captioner model to process and label a large video database, wherein the captioner model generates metadata for new video content based on learned cinematic elements;
• and
• post-process the generated labels to ensure consistency and accuracy, including performing outlier detection, confidence scoring, and manual quality control.