Delta Distillation for Video Neural Network Inference
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Solution Overview
Problem
Artificial neural networks process video frames independently, failing to leverage temporal redundancies, leading to inefficient computational complexity and resource usage.
Innovation Solution
Implement a teacher-student neural network paradigm where the student network is trained on deltas (differences) between frames, leveraging temporal redundancies to reduce computational complexity and resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If artificial neural networks process each video frame independently, then each frame can be processed without temporal dependencies, but computational complexity grows at a higher rate than the rate at which information in the video content grows
Solution Approach 1:
The video processing is segmented into two distinct networks: a teacher network that processes the first frame to generate feature maps, and a student network that processes only the difference (delta) between frames. This segmentation allows the system to handle temporal redundancies by separating full-frame processing from delta processing, thereby reducing overall computational complexity while maintaining processing efficiency.
Solution Approach 2:
The patent extracts only the essential information from each frame by computing the difference (delta) between successive frames. Instead of processing complete frames through the student network, only the changes are extracted and processed, which significantly reduces the amount of data the student network must handle while preserving the temporal information needed for accurate video understanding.
2Measurement precision
If artificial neural networks process repeated data between frames, then complete frame information is processed, but this is highly inefficient since only a small amount of data changes during elapsed time between frames
Solution Approach 1:
The student network uses a copied and adapted version of the teacher network's architecture but processes only the delta input rather than complete frames. This copying approach allows the student network to leverage the teacher network's feature extraction capabilities while operating on reduced data, thereby maintaining measurement precision for video content analysis while significantly reducing computational resource usage.
3Device complexity
If a student neural network is trained on differences between frames, then temporal redundancies are leveraged to reduce computational complexity, but the network must be trained to accurately predict frame changes
Solution Approach 1:
The training process incorporates feedback mechanisms where the student network's predictions of frame differences are compared against actual frame differences, and the network weights are adjusted accordingly. This feedback loop ensures that the student network learns to accurately predict temporal changes while maintaining reduced computational complexity during inference.
Solution Approach 2:
The teacher network performs preliminary processing of the first frame to generate feature maps before the student network processes subsequent frames. This preliminary action provides a solid foundation for the student network to build upon, ensuring that even though it processes only deltas, the overall system maintains high prediction accuracy by combining the teacher's comprehensive initial analysis with the student's efficient temporal processing.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques and apparatus for processing video content using an artificial neural network. An example method generally includes receiving a video data stream including at least a first frame and a second frame. First features are extracted from the first frame using a teacher neural network. A difference between the first frame and the second frame is determined. Second features are extracted from at least the difference between the first frame and the second frame using a student neural network. A feature map for the second frame is generated based a summation of the first features and the second features. An inference is generated for at least the second frame of the video data stream based on the generated feature map for the second feature.