Cross-Modal Caption Transfer for Scalable Video and Audio Annotation
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Solution Overview
Problem
The lack of large-scale, high-quality training data for video and audio captioning systems poses challenges in efficiently training machine-learned models, as manual annotation is time-consuming and costly, and existing methods like Automatic Speech Recognition (ASR) often produce inaccurate and misaligned captions.
Innovation Solution
An automated annotation system transfers captions from image captioning datasets to video and audio clips using cross-modal transfer, leveraging image supervision to label video and audio data without human intervention, creating scalable and diverse datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation methods are used to label video frames, then annotation accuracy can be maintained, but time consumption and computational expenditure increase significantly
Solution Approach 1:
The system performs preliminary actions by pre-processing video frames to extract visual features and organizing them in advance. Caption templates are prepared beforehand with associated visual feature vectors, enabling rapid matching during inference without time-consuming manual annotation during deployment.
Solution Approach 2:
The system uses template matching where pre-defined caption templates with associated visual feature vectors are copied and applied to matching video frames. This allows automated generation of accurate captions by copying proven templates rather than creating new annotations manually for each frame.
2Quantity of substance
If more video data is collected for training, then model performance improves, but data labeling costs increase prohibitively
Solution Approach 1:
The system copies proven caption templates to multiple matching video frames, enabling automated labeling of large volumes of training data without proportional increases in manual labeling costs. Once a caption template is validated, it can be copied to numerous similar frames.
Solution Approach 2:
The system changes parameters by adjusting the matching threshold and template selection criteria to optimize the balance between automated labeling accuracy and coverage. This allows scalable data annotation by tuning parameters rather than manually labeling each frame.
3Loss of information
If frame-by-frame review is performed to capture event transitions, then annotation completeness improves, but productivity decreases due to redundancy
Solution Approach 1:
The system performs preliminary feature extraction on video frames, organizing visual features in advance to enable efficient comparison against template vectors. This pre-processing allows rapid detection of event transitions without reviewing each frame in detail.
Solution Approach 2:
The system uses template matching to detect event transitions by comparing video frame features against stored caption templates. When a template match is found, the corresponding caption is automatically applied, enabling efficient detection of event transitions without manual frame-by-frame review.
4Measurement precision
If higher quality image data is used for training, then recognition accuracy improves, but data acquisition and processing costs increase
Solution Approach 1:
The system copies visual feature vectors from video frames to matching caption templates, enabling efficient use of existing high-quality visual data without requiring additional computational resources for data generation or enhancement.
Solution Approach 2:
The system optimizes the balance between data quality and computational cost by adjusting matching thresholds and template selection parameters. This allows the system to achieve high recognition accuracy using appropriately matched templates rather than requiring all possible high-quality variations.
Data Source
AI summary
Techniques for improving the performance of video retrieval systems and audio retrieval systems are described herein. A computing system can obtain a captioned image with an associated caption and a first video having a plurality of frames. Additionally, the system can determine a feature vector of the captioned image and a feature vector of a first frame in the plurality of frames. Moreover, the system can calculate a similarity value between the captioned image and the first frame based on the feature vector of the captioned image and the feature vector of the first frame. Furthermore, the system can transfer the associated caption to the first frame based on the similarity value. Subsequently, the system can generate a video clip based on the first frame. The system can also store and index the video clip in a video captioning database.