Video Captioning Pipeline Using Cross-Modality Teachers at Scale
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Solution Overview
Problem
Collecting high-quality video-text data for large-scale multimodal learning is challenging due to the time-consuming nature of manual video annotation, the difficulty in aligning meta-information with video content, and the limitations of existing datasets, which often fail to capture the main content and actions accurately.
Innovation Solution
An automatic captioning pipeline that utilizes a combination of multimodal data, including video frames, subtitles, and descriptions, to generate high-quality video-caption pairs by applying multiple cross-modality teacher models and a student captioning model trained through knowledge distillation, leveraging both visual and textual inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual video annotation is used, then high-quality video-text data can be collected, but the process is time-consuming and difficult to scale
Solution Approach 1:
The patent introduces teacher models as intermediaries that generate pseudo-labels for videos, which then serve as training data for student models. This intermediary approach allows automatic generation of high-quality annotations at scale, resolving the contradiction between annotation quality and data collection efficiency.
Solution Approach 2:
The patent creates copies of annotation patterns by training teacher models on manually annotated data, then using these models to generate numerous pseudo-labeled video-text pairs. This copying mechanism enables scalable production of high-quality annotations without manual intervention for each video.
2Manufacturing precision
If multiple teacher models are used to generate captions, then data quality improves, but computational complexity and processing time increase
Solution Approach 1:
The patent segments the captioning task by using multiple specialized teacher models, each potentially optimized for different aspects of video understanding. This segmentation allows the system to leverage diverse strengths of different models while maintaining manageable individual model complexities.
Solution Approach 2:
The patent merges the outputs of multiple teacher models through aggregation and selection processes to generate final captions. This combining approach consolidates the strengths of multiple models while managing complexity through systematic integration rather than creating a single overly complex model.
3Productivity
If existing datasets are used, then training can begin quickly, but they fail to capture main content and actions accurately
Solution Approach 1:
The patent performs preliminary action by pre-training teacher models on existing datasets to establish baseline captioning capabilities. This preliminary training enables quick initialization while the subsequent pseudo-labeling phase on target videos refines accuracy for main content and actions.
Solution Approach 2:
The system performs self-service by using the teacher models to automatically generate high-quality pseudo-labels for training data without requiring external manual annotation. This self-service mechanism simultaneously achieves fast training initiation and high content accuracy.
Data Source
AI summary
Automatic captioning pipelines and methods for automatically annotating video data with subtitles, which can be obtained using automatic speech recognition (ASR). An automatic captioning pipeline with inputs of multimodal data scales up the dataset of high-quality video-caption pairs. The automatic captioning pipeline generates video-caption pairs by establishing and using a large video-language dataset along with an automatic captioning approach leveraging multimodal inputs, such as textual video description, subtitles, and individual video frames.