Video-Text Classification Model Adaptation via Weight Fusion
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Solution Overview
Problem
Conventional image-text classification models struggle to adapt to video classification due to the temporal dimension in videos, leading to limitations in recognizing visual content not represented in the training dataset, known as the zero-shot problem, and require significantly smaller training datasets which restrict their ability to generalize.
Innovation Solution
A model training system that leverages a pretrained image-text classification model to generate a video-text classification model by assigning textual labels to unlabeled videos, using contrastive and distillation losses to refine the model's weights, and fusing them with the pretrained model's weights to adapt to the video domain, enabling accurate classification of videos and texts even outside the training data distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional image-text classification models are used for video classification, then the models can leverage existing training data, but they fail to recognize visual content not represented in the training dataset (zero-shot problem)
Solution Approach 1:
The system performs preliminary actions by generating synthetic training data using the pretrained image-text classification model before actual video classification. Pseudolabeled videos are created by processing unlabeled videos through the pretrained model, and this synthetic data is used to train a video-specific classification model in advance, enabling zero-shot capability while maintaining reliability.
Solution Approach 2:
The system introduces an intermediary video-specific classification model that acts as a bridge between the pretrained image-text model and actual video classification tasks. This intermediary model is trained on synthetically generated video data and handles the temporal dimension specific to videos, while the pretrained model provides general visual understanding for zero-shot scenarios.
2Adaptability or versatility
If auxiliary data is used to teach the model distinguishing properties of objects, then the model can address zero-shot problems, but conventional approaches do not reliably extend to video classification due to temporal dimension characteristics
Solution Approach 1:
The system applies local quality by creating video-specific training data with appropriate temporal characteristics. Instead of using generic auxiliary data, the system generates pseudolabeled videos that preserve temporal sequences and motion information specific to video data, allowing the model to learn domain-specific properties while maintaining general zero-shot capability.
3Productivity
If the model is trained on a small training dataset, then training efficiency is improved, but the model's ability to generalize is restricted
Solution Approach 1:
The system uses copying by generating synthetic video training data through the pretrained image-text classification model. Instead of requiring extensive real labeled video data, the system creates pseudolabeled videos by processing unlabeled videos through the pretrained model, effectively copying and adapting image-level knowledge to the video domain while maintaining training efficiency.
Data Source
AI summary
A model training system is described that obtains a training dataset including videos and text labels. The model training system generates a video-text classification model by causing a model having a dual image text encoder architecture to predict which of the text labels describes each video in the training dataset. Predictions output by the model are compared to the training dataset to determine distillation and contrastive losses, which are used to adjust internal weights of the model during training. The internal weights of the model are then combined with internal weights of a trained image-text classification model to generate the video-text classification model. The video text-classification model is configured to generate a video or text output that classifies a video or text input.