Incremental Video Highlight Detection With Prototype Distillation
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Solution Overview
Problem
Current video highlights detection methods require retraining the model with all collected videos when new highlight categories are added, leading to high computational costs and potential degradation in accuracy for existing categories due to the closed world assumption.
Innovation Solution
An incremental video highlights detection system that incrementally trains a convolutional neural network, transformer encoder, and feedforward network model using data and model distillation, allowing the system to learn from new categories without retraining from scratch, by leveraging previous weights and using a minimax loss function to update prototype distances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the model is retrained using all collected videos when new highlight categories are added, then the model can detect new categories, but the computational cost and energy consumption increase significantly
Solution Approach 1:
The training process is segmented into incremental stages where the model is trained on new categories separately rather than retraining on all data at once. This allows the system to adapt to new categories while maintaining lower computational costs by focusing training resources on specific new categories rather than performing full retraining.
Solution Approach 2:
The system performs preliminary actions by maintaining and updating category prototypes incrementally as new categories are introduced. The prototype-based approach allows the model to prepare for new categories by learning representative features in advance, reducing the computational burden when actual detection is needed.
2Adaptability or versatility
If the model is retrained using all collected videos when new highlight categories are added, then the model can detect new categories, but the accuracy for original highlight categories degrades
Solution Approach 1:
The system creates and maintains separate prototype representations for each category that can be independently updated. When new categories are added, the prototypes for original categories are preserved and copied unchanged, preventing any degradation in their classification accuracy while the new categories receive their own prototype initializations.
Solution Approach 2:
The model applies different quality treatments to different categories: original category prototypes are maintained with high fidelity and minimal changes, while new categories receive focused training resources. This local differentiation ensures that improvements in new category detection do not come at the expense of original category accuracy.
3Adaptability or versatility
If the model is retrained using all collected videos when new highlight categories are added, then the model can detect new categories, but the scalability with respect to increasing highlight category domains and training data is poor
Solution Approach 1:
The system dynamically adapts its training process based on the current set of categories. Rather than following a fixed retraining schedule, the model incrementally updates prototypes as categories are added, allowing the training process to scale efficiently with the number of categories. This dynamic approach enables the system to handle increasing category domains without linearly increasing training costs.
Data Source
AI summary
Systems and methods are provided that include a processor executing a video classifying program to receive an input video, sample video frames from the input video, extract frame-wise spatial features from the video frames using a convolutional neural network, extract a frame-wise temporal feature for each video frame, aggregate the frame-wise spatial features and the frame-wise temporal feature for each video frame to provide a temporal context to the frame-wise spatial features, input the aggregated frame-wise spatial features and the frame-wise temporal feature for each frame into a transformer encoder to obtain temporal-aware feature representations of the video frames, input the feature representations into a feedforward network model to obtain feedforward-transformed features, obtain a parameter by inputting each feedforward-transformed feature and a set of highlight prototypes into a function comparing the feedforward-transformed features to the set of highlight prototypes, classify the video frames as highlights based on the calculated parameter.