Method, system, and computer-readable medium for training a captioner model to generate captions for video content by analyzing and predicting cinematic elements
A method trains a captioner model to generate captions for video content by organizing a dataset, extracting frames, associating metadata, segmenting video, applying labels, aggregating labels, training the model, refining it, deploying it for labeling, and post-processing labels. A computing system trains a captioner model by organizing datasets, extracting frames, associating metadata, segmenting videos, applying labels, aggregating labels, training the model, refining it, deploying it for labeling, and post-processing labels. A computer-readable medium has instructions for training a captioner model by organizing datasets, extracting frames, associating metadata, segmenting videos, applying labels, aggregating labels, training the model, refining it, deploying it for labeling, and post-processing labels.
1 . A computer-implemented method for training a captioner model to generate captions for video content based on cinematic elements, the computer-implemented method comprising:
organizing 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;
extracting frames from the raw video clips at a consistent frame rate and storing the frames in a structured format;
associating 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;
segmenting 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;
applying 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;
aggregating the frame-level labels to generate shot-level labels, wherein the aggregating includes calculating average focal length, determining predominant framing style, and smoothing camera movement data across the shot;
training 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 refining the captioner model based on feedback from validation datasets to improve accuracy of cinematic element prediction;
deploying the trained captioner model to process and label a large video database, wherein the model generates metadata for new video content based on learned cinematic elements; and
post-processing the generated labels to ensure consistency and accuracy, including performing outlier detection, confidence scoring, and manual quality control.
2 . The method of claim 1 , wherein organizing the dataset further comprises storing the video clips in a high-resolution, lossless format to preserve visual information critical for model learning.
3 . The method of claim 1 , wherein the metadata associated with each frame is stored in formats selected from the group consisting of JSON and CSV.
4 . The method of claim 1 , wherein segmenting the raw video clips into shots is performed using shot detection algorithms that identify scene changes.
5 . The method of claim 1 , wherein applying labels to each frame further comprises using LiDAR and laser locator data to provide highly accurate, frame-by-frame positioning of the camera and objects within a scene.
6 . The method of claim 1 , wherein aggregating frame-level labels to generate shot-level labels further comprises using majority voting to determine the predominant framing style for the shot.
7 . The method of claim 1 , further comprising conditioning the captioner model on both visual features extracted from video frames and the detailed captioned metadata during training.
8 . The method of claim 1 , further comprising using a multi-task learning approach during training, wherein the captioner model is trained to minimize losses for both visual quality and cinematic accuracy.
9 . The method of claim 1 , further comprising employing active learning techniques to iteratively fine-tune the captioner model based on difficult cases identified during training.
10 . The method of claim 1 , further comprising exporting final labels generated by the captioner model to a structured format for easy integration into downstream tasks, including database storage and video editing software.
11 . 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.
12 . The computing system of claim 11 , the memories having stored thereon further instructions that, when executed, cause the computing system to store the video clips in a high-resolution, lossless format to preserve visual information critical for model learning.
13 . The computing system of claim 11 , the memories having stored thereon further instructions that, when executed, cause the computing system to store the metadata associated with each frame in formats selected from the group consisting of JSON and CSV.
14 . The computing system of claim 11 , the memories having stored thereon further instructions that, when executed, cause the computing system to perform segmenting the raw video clips into shots using shot detection algorithms that identify scene changes.
15 . The computing system of claim 11 , the memories having stored thereon further instructions that, when executed, cause the computing system to use LiDAR and laser locator data to provide highly accurate, frame-by-frame positioning of the camera and objects within a scene when applying labels to each frame.
16 . The computing system of claim 11 , the memories having stored thereon further instructions that, when executed, cause the computing system to use majority voting to determine the predominant framing style for the shot when aggregating frame-level labels to generate shot-level labels.
17 . The computing system of claim 11 , the memories having stored thereon further instructions that, when executed, cause the computing system to condition the captioner model on both visual features extracted from video frames and the detailed captioned metadata during training.
18 . The computing system of claim 11 , the memories having stored thereon further instructions that, when executed, cause the computing system to use a multi-task learning approach during training, wherein the captioner model is trained to minimize losses for both visual quality and cinematic accuracy.
19 . The computing system of claim 11 , the memories having stored thereon further instructions that, when executed, cause the computing system to employ active learning techniques to iteratively fine-tune the captioner model based on difficult cases identified during training.
20 . The computing system of claim 11 , the memories having stored thereon further instructions that, when executed, cause the computing system to export final labels generated by the captioner model to a structured format for easy integration into downstream tasks, including database storage and video editing software.
21 . 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.
22 . The computer-readable medium of claim 21 having stored thereon further instructions that, when executed, cause a computer to store the video clips in a high-resolution, lossless format to preserve visual information critical for model learning.
23 . The computer-readable medium of claim 21 having stored thereon further instructions that, when executed, cause a computer to store the metadata associated with each frame in formats selected from the group consisting of JSON and CSV.
24 . The computer-readable medium of claim 21 having stored thereon further instructions that, when executed, cause a computer to segment the raw video clips into shots using shot detection algorithms that identify scene changes.
25 . The computer-readable medium of claim 21 having stored thereon further instructions that, when executed, cause a computer to apply labels to each frame using LiDAR and laser locator data to provide highly accurate, frame-by-frame positioning of the camera and objects within a scene.
26 . The computer-readable medium of claim 21 having stored thereon further instructions that, when executed, cause a computer to aggregate frame-level labels to generate shot-level labels using majority voting to determine the predominant framing style for the shot.
27 . The computer-readable medium of claim 21 having stored thereon further instructions that, when executed, cause a computer to condition the captioner model on both visual features extracted from video frames and the detailed captioned metadata during training.
28 . The computer-readable medium of claim 21 having stored thereon further instructions that, when executed, cause a computer to use a multi-task learning approach during training, wherein the captioner model is trained to minimize losses for both visual quality and cinematic accuracy.
29 . The computer-readable medium of claim 21 having stored thereon further instructions that, when executed, cause a computer to employ active learning techniques to iteratively fine-tune the captioner model based on difficult cases identified during training.
30 . The computer-readable medium of claim 21 having stored thereon further instructions that, when executed, cause a computer to export final labels generated by the captioner model to a structured format for easy integration into downstream tasks, including database storage and video editing software.