Video Caption Pre-Training Using Unlabeled Frames and Utterances
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Solution Overview
Problem
The lack of large-scale, manually annotated video data for training video understanding models is a significant challenge due to the time-intensive and subjective nature of caption annotation, which contrasts with the abundance of image classification data.
Innovation Solution
A method is proposed to pre-train a machine learning model using unlabeled video data by leveraging two textual streams—input to the encoder and captioning target for the decoder—through forward and backward generation losses, enabling joint optimization of the encoder and decoder without labeled data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manually annotated video data is used for training, then the model can achieve accurate video understanding, but the annotation process is time-intensive and expensive
Solution Approach 1:
The patent applies preliminary action by pre-training the model on large-scale unlabeled video data before fine-tuning on smaller labeled datasets. This preliminary pre-training phase enables the model to learn general video representations without requiring time-intensive manual annotations, thereby reducing the overall annotation time while maintaining caption accuracy through subsequent targeted fine-tuning.
2Measurement precision
If manually annotated video data is used for training, then the model can achieve accurate video understanding, but the annotation process is expensive
Solution Approach 1:
The patent performs preliminary pre-training on abundant unlabeled video data, which eliminates the need for expensive manual annotation for the majority of training data. This approach significantly reduces annotation costs while maintaining model performance, as the model learns general video understanding capabilities from unlabeled data before being fine-tuned on a smaller labeled subset.
Solution Approach 2:
The patent uses automatically generated captions from automatic speech recognition (ASR) systems as substitutes for manual annotations. These generated captions serve as cheap copies that capture the essential semantic information needed for training, dramatically reducing the cost of obtaining training data while still enabling the model to learn effective video representations.
3Measurement precision
If manually annotated video data is used for training, then the model can achieve accurate video understanding, but the annotation process is subjective with low inter-annotator agreement
Solution Approach 1:
The patent replaces subjective manual annotations with automatically generated captions from ASR systems. These automated captions provide objective, consistent training signals that eliminate inter-annotator variability. The model learns from these reliable automated transcripts, improving the reliability and consistency of the training data while maintaining caption quality through the pre-training-fine-tuning paradigm.
4Adaptability or versatility
If large-scale unlabeled video data is used for pre-training, then the model can learn general video representations, but the model needs to be fine-tuned for specific tasks
Solution Approach 1:
The patent segments the training process into two distinct phases: pre-training on large-scale unlabeled data to learn general video representations, and fine-tuning on smaller labeled datasets for specific tasks. This segmentation allows the model to first acquire broad video understanding capabilities universally, then adapt to specific tasks without requiring complete retraining, thereby managing training complexity while maintaining versatility.
Data Source
AI summary
Systems and methods for performing captioning for image or video data are described herein. The method can include receiving unlabeled multimedia data, and outputting, from a machine learning model, one or more captions for the multimedia data. Training the machine learning model to create these outputs can include inputting a subset of video frames and a first utterance into the machine learning model, using the machine learning model to predict a predicted utterance based on the subset of video frames and the first utterance, and updating one or more parameters of the machine learning model based on a loss function that compares the predicted utterance with the second utterance.