Deep Learning Video Captioning via Self-Supervised Pretraining
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Solution Overview
Problem
Existing machine learning technologies face challenges in extracting information from videos due to the high cost of expert labeling and the inadequacy of labeled data, especially when applying video information extraction techniques.
Innovation Solution
A deep learning-based video information extraction system is developed, which includes a memory to store videos, a textual information extraction module to obtain information from ground truth captions, and a video captioning module with an encoder and decoder to generate text captions based on video features and extracted information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If expert labeling is used to train classifiers for video information extraction, then the accuracy and reliability of the model parameters improve, but the cost and time required become prohibitively expensive
Solution Approach 1:
The patent applies preliminary action by pre-training the deep learning model on large amounts of unlabeled video data using self-supervised learning techniques. This preliminary training phase allows the model to learn useful representations without requiring expensive expert labels, and then only a small amount of labeled data is needed for fine-tuning, thus resolving the contradiction between model accuracy and labeling time
Solution Approach 2:
The patent implements self-service through self-supervised learning where the model generates its own training signals from the video data without human intervention. The model learns to predict future frames, reconstruct corrupted inputs, or solve other self-generated tasks, eliminating the need for expensive expert labeling while still achieving reliable model parameters
2Measurement precision
If more labeled video data is collected to improve model training, then the estimation accuracy of model parameters improves, but the cost and resource requirements increase significantly
Solution Approach 1:
The system uses self-supervised learning to generate training signals automatically from the video data itself, eliminating the need for large volumes of expensive labeled data. The model learns useful representations by solving self-generated tasks such as frame prediction or reconstruction, achieving good parameter estimation with minimal labeled data
Solution Approach 2:
The patent applies preliminary action by pre-training the model on large amounts of unlabeled video data before fine-tuning with a small labeled dataset. This two-stage approach allows the model to learn general video representations from unlabeled data, reducing the amount of labeled data needed for accurate parameter estimation
3Difficulty of detecting and measuring
If traditional object detection methods are used for video analytics, then the focus remains on detecting objects, but the extraction of comprehensive information such as terms, entities, relations, and events becomes challenging
Solution Approach 1:
The patent applies universality by designing a video captioning model that performs multiple functions simultaneously: it detects objects, extracts entities, identifies relations, and generates comprehensive event descriptions. The unified deep learning framework processes video input and produces rich semantic information in the form of natural language captions, resolving the limitation of traditional single-function object detection methods
Data Source
AI summary
A video information extraction system includes a memory to store a video; a textual information extraction module to obtain information about terms, entities, relations, and events from a ground truth caption corresponding to the video; and a video captioning module including an encoder (i) to receive the information about the terms, entities, relations, and events from the textual information extraction module, and (ii) to extract video features from the video; and a decoder to generate a text caption based on the extracted video features.