Video Domain Adaptation Using Pseudo-Labels Without Target Annotation
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Solution Overview
Problem
Machine learning systems trained on labeled data in one domain perform poorly when applied to unlabeled data in a different domain due to differences in domain perspectives, leading to reduced performance in video analysis tasks.
Innovation Solution
A video analysis model is trained using contrastive learning with a loss function that includes cross-domain and cross-modality regularization, leveraging pseudo-labels and feature memories to bridge domains and modalities, enhancing feature representations for effective knowledge transfer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a video analysis model is trained on labeled data in a first domain, then the model can perform video analysis tasks in that domain, but the model performance deteriorates when applied to unlabeled data in a different domain due to domain differences
Solution Approach 1:
The patent introduces an intermediary contrastive learning framework that bridges the first domain (with labels) and second domain (without labels). The framework uses pseudo-labels as intermediaries to connect the two domains, allowing knowledge transfer without direct labeled data in the target domain. The contrastive loss function acts as a mediator to align feature representations across domains and modalities, resolving the performance deterioration issue when applying models across different video domains.
2Measurement precision
If labeled training data is used for model training, then model accuracy improves, but the annotation process becomes labor-intensive and time-consuming
Solution Approach 1:
The patent implements self-service through automatic pseudo-label generation for the second domain data. The model generates its own training labels without human intervention by leveraging the contrastive learning framework and feature alignments. This eliminates the need for manual annotation in the target domain, saving significant time and resources while maintaining model accuracy through the unsupervised contrastive learning process.
3Adaptability or versatility
If contrastive learning with cross-domain and cross-modality regularization is used, then knowledge transfer between domains improves, but the model complexity increases
Solution Approach 1:
The patent segments the complex training process into distinct modular components: (1) feature extraction modules for different modalities, (2) pseudo-label generation module, (3) contrastive loss computation with separate cross-domain and cross-modality regularization terms, and (4) optimization module. This segmentation makes the complex contrastive learning framework more manageable and implementable while maintaining strong knowledge transfer capabilities across domains and modalities.
Data Source
AI summary
Video methods and systems include extracting features of a first modality and a second modality from a labeled first training dataset in a first domain and an unlabeled second training dataset in a second domain. A video analysis model is trained using contrastive learning on the extracted features, including optimization of a loss function that includes a cross-domain regularization part and a cross-modality regularization part.