Federated Video Analytics Models for Unseen Domain Generalization
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Solution Overview
Problem
Existing video analytics models struggle with inefficiency in new or unseen scenarios due to prioritizing variant features over invariant features, leading to out-of-distribution generalization issues and high computational intensity in data generation, particularly in Generative Adversarial Networks (GANs).
Innovation Solution
A federated learning-based approach with a parent-child model architecture is employed, using adaptive weight transfer and knowledge distillation to learn invariant features across multiple datasets with different underlying distributions, leveraging pre-trained models for feature extraction and hierarchical clustering to generate a robust model for video analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing models prioritize variant features for easy learning, then learning efficiency is improved, but generalization performance deteriorates in new or unseen scenarios
Solution Approach 1:
The model architecture is segmented into multiple specialized models (e.g., domain-specific models and general-purpose models) that process different types of features separately. This segmentation allows the system to efficiently learn variant features in domain-specific models while maintaining generalization capability through general-purpose models that focus on invariant features.
Solution Approach 2:
Different parts of the system are assigned different functional qualities: domain-specific models are optimized for learning variant features efficiently, while general-purpose models are optimized for capturing invariant features for generalization. This local quality differentiation resolves the contradiction by allowing each component to excel at its specific function.
2Adaptability or versatility
If Generative Adversarial Networks are used for data generation, then data diversity is improved, but computational intensity increases significantly
Solution Approach 1:
Instead of using computationally intensive GANs to generate diverse data, the system creates simplified copies or representations of existing data through feature extraction and transformation. This approach maintains data diversity by generating varied feature representations while avoiding the high computational cost of GAN-based data generation.
3Measurement precision
If domain specific models are trained for specific domains, then performance in known domains is improved, but adaptability to new domains deteriorates
Solution Approach 1:
The system employs a multi-functional architecture where general-purpose models serve multiple domains by capturing universal invariant features, while domain-specific models handle domain-particular variant features. This universal design allows the system to maintain high performance in known domains while adapting to new domains through the general-purpose models.
Solution Approach 2:
The system performs preliminary learning of invariant features that span across multiple domains before tackling domain-specific variant features. This preliminary action establishes a foundation of generalizable knowledge that enables rapid adaptation to new domains while maintaining performance in known domains.
Data Source
AI summary
A system and method for generating federated learning-based model for improved video analytics is provided. A set of datasets are fetched from an input unit that comprises video data associated with domain stored in the input unit. Features are extracted from the pre-defined number of frames with respect to an event captured in the video frames by employing a pre-trained model on pre-processed video frames. Video data is passed through the pre-trained model to obtain video-specific features in the form of vectors. Video data is clustered with similar underlying distributions in the same cluster. Lastly, parent-child model architecture is generated. Parent model data is initialized for parent model generation by aggregating a first pre-defined percentage of video data from each cluster in stratified manner. The child models are initialized after generating parent model by using weights of the parent model for predicting event associated with the domain.