Video Captioning Pipeline Using Cross-Modality Teacher Distillation
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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 lack of precise temporal alignment of meta-information, and the limitations of existing datasets, which often fail to capture the main content of videos.
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 model trained through knowledge distillation, leveraging a two-branch architecture for efficient annotation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual video annotation is used, then annotation quality can be ensured, but the process becomes extremely time-consuming and cannot scale to large volumes of video data
Solution Approach 1:
The patent uses teacher models to generate caption candidates that copy and generalize from manually annotated video-caption pairs. The teacher models are trained on manually annotated data and then apply this knowledge to automatically annotate large volumes of video data, preserving annotation quality while enabling scaling.
Solution Approach 2:
The patent introduces teacher models as intermediaries between manually annotated data and automatically annotated data. These teacher models serve as mediators that transfer annotation knowledge from small-scale high-quality data to large-scale automated annotation, resolving the contradiction between quality and productivity.
2Manufacturing precision
If multiple teacher models are used to generate caption candidates, then caption quality improves, but computational complexity and processing time increase
Solution Approach 1:
The patent segments the caption generation process into multiple independent teacher models, each specializing in different aspects of video understanding. This segmentation allows parallel processing of different video clips by different teacher models, improving throughput while maintaining quality through ensemble methods.
Solution Approach 2:
The patent merges the outputs of multiple teacher models through ensemble methods, combining their individual caption candidates to produce final annotations. This merging process leverages the strengths of different models while distributing computational load, managing complexity through coordinated integration.
3Productivity
If video data is divided into multiple clips for processing, then processing efficiency improves, but temporal alignment and semantic consistency become more difficult to maintain
Solution Approach 1:
The patent performs preliminary actions by training teacher models on manually annotated video-caption pairs before automated processing. This preliminary training establishes temporal alignment and semantic consistency in the models themselves, allowing them to maintain these properties during subsequent automated processing of divided video clips without requiring re-synchronization at each processing stage.
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.