Sample-Adaptive Cross-Layer Norm Calibration for Video Analysis
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Solution Overview
Problem
Deep learning networks, such as CNNs, face challenges in image sequence/video analysis due to complex feature distribution variations and high memory and compute demands, leading to difficulties in training and slow convergence, especially under restricted mini-batch size settings.
Innovation Solution
The implementation of Sample-Adaptive Cross-Layer Norm Calibration and Relay (CLN-CR) technology, which dynamically calibrates feature tensors for each video sample and uses a relay mechanism to associate calibration parameters across neighboring layers, improving feature representation and training speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If deep CNNs are used for image sequence/video analysis, then modeling capability for complex feature distribution is improved, but training convergence difficulty and time increase significantly
Solution Approach 1:
The patent changes the normalization parameters from batch-level statistics to sample-level dynamic calibration. By computing calibration parameters for each individual video sample rather than averaging across mini-batches, the system adapts to complex feature distributions without requiring large batch sizes, thus reducing training time while maintaining modeling capability.
Solution Approach 2:
The patent introduces dynamic sample-adaptive calibration parameters that are computed and updated during training for each video sample. This dynamic approach allows the normalization layer to adapt to varying feature distributions in real-time, improving convergence speed while maintaining the ability to handle complex temporal patterns in video data.
2Quantity of substance
If mini-batch size is restricted, then memory and compute demand is reduced, but statistics estimation accuracy deteriorates
Solution Approach 1:
The patent introduces sample-adaptive calibration parameters as an intermediary mechanism that bridges the gap between small mini-batch sizes and accurate statistics estimation. Instead of directly using mini-batch statistics, the system computes calibration parameters for each sample and then aggregates them, effectively mediating between limited batch size and accurate normalization statistics.
Solution Approach 2:
The patent performs preliminary calibration parameter computation for each individual video sample before aggregation. By pre-computing sample-specific calibration parameters, the system ensures accurate statistics estimation even when the final mini-batch size is small, as each sample contributes its own calibrated statistics to the overall normalization.
3Measurement precision
If dynamic sample-adaptive calibration is implemented, then feature representation accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent merges the calibration parameter computation into the existing normalization layer operations. By combining sample-adaptive calibration with the standard normalization process in a unified computational framework, the system improves feature representation accuracy while avoiding the overhead of separate computational passes, thus managing computational complexity efficiently.
Data Source
AI summary
Technology to conduct image sequence/video analysis can include a processor, and a memory coupled to the processor, the memory storing a neural network, the neural network comprising a plurality of convolution layers, and a plurality of normalization layers arranged as a relay structure, wherein each normalization layer is coupled to and following a respective one of the plurality of convolution layers. The plurality of normalization layers can be arranged as a relay structure where a normalization layer for a layer (k) is coupled to and following a normalization layer for a preceding layer (k−1). The normalization layer for the layer (k) is coupled to the normalization layer for the preceding layer (k−1) via a hidden state signal and a cell state signal, each signal generated by the normalization layer for the preceding layer (k−1). Each normalization layer (k) can include a meta-gating unit (MGU) structure.