Video Understanding Model Pretraining for Accuracy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current machine-learning models for video classification, particularly transformer-based models, require large datasets for effective training, which is costly and challenging due to the scarcity of large video datasets compared to image datasets, leading to prohibitively difficult training of models to high accuracy.
Innovation Solution
The approach involves initializing video understanding models from pretrained image models, leveraging self-attention mechanisms and factorizing the encoder to efficiently process spatiotemporal tokens, and utilizing smaller video datasets for training, thereby reducing computational complexity and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If transformer-based models are used for video classification, then classification accuracy can be improved, but the requirement for large datasets increases computational complexity and training difficulty
Solution Approach 1:
The patent applies preliminary action by pre-training the transformer model on image datasets before fine-tuning on video datasets. This allows the model to learn useful representations from abundant image data first, reducing the amount of video data needed for effective training and lowering overall training complexity while maintaining high classification accuracy.
Solution Approach 2:
The patent segments the training process into two distinct phases: pre-training on image data and fine-tuning on video data. This segmentation allows the model to leverage the vast amount of available image data for initial training, then adapt to video-specific tasks with smaller datasets, thereby reducing the computational burden of training solely on video data.
2Measurement precision
If large video datasets are used for training, then model accuracy improves, but data collection and processing costs increase
Solution Approach 1:
The patent applies universality by using a pre-trained image model that can serve dual purposes: it was originally trained on image data for image classification tasks, and then reused for video classification tasks. This multi-functionality allows the model to leverage the same foundational representations for both domains, reducing the need for exclusively large video datasets.
Solution Approach 2:
The patent uses an intermediary approach by introducing a pre-trained image model as a starting point. This intermediary model provides a foundation that can be fine-tuned for video tasks, acting as a bridge between the abundant image data ecosystem and the more scarce video data requirements, thereby reducing the quantity of video data needed for effective training.
3Measurement precision
If video transformer encoder models are trained from scratch, then accuracy on video tasks improves, but training time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by performing pre-training on image data before the actual video classification training. This preliminary training phase establishes useful feature representations and attention mechanisms that can be directly transferred to video tasks, significantly reducing the training time required when working with video data compared to training from scratch.
Solution Approach 2:
The patent applies parameter changes by initializing the video transformer model with parameters from the pre-trained image model rather than random initialization. This parameter transfer allows the model to start with useful weights already in place, reducing the number of training iterations needed and overall training time while achieving high accuracy on video classification tasks.
Data Source
AI summary
A computer-implemented method for classifying video data with improved accuracy includes obtaining, by a computing system comprising one or more computing devices, video data comprising a plurality of video frames; extracting, by the computing system, a plurality of video tokens from the video data, the plurality of video tokens comprising a representation of spatiotemporal information in the video data; providing, by the computing system, the plurality of video tokens as input to a video understanding model, the video understanding model comprising a video transformer encoder model; and receiving, by the computing system, a classification output from the video understanding model.