Sample-adaptive 3D Feature Calibration Agent for Video Analysis
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Solution Overview
Problem
Three-dimensional (3D) convolutional neural networks (CNNs) face challenges due to increased input data dimensionality and cubic model growth, leading to high memory and compute demands, making them less viable for high-performance image sequence/video analysis compared to two-dimensional (2D) CNNs.
Innovation Solution
The introduction of a sample-adaptive feature calibration and association agent (SA-FCAA) that calibrates 3D feature maps using statistics from adjacent layers and dimensions, and a meta-gating relay unit to associate features across orthogonal dimensions, enhancing joint spatiotemporal feature learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If 3D CNN architecture is used for image sequence/video analysis, then spatiotemporal feature learning capability is improved, but model size and compute demands increase cubically
Solution Approach 1:
The patent segments the 3D feature calibration process into two independent dimensions: network depth calibration (calibrating across different convolutional layers) and feature dimension calibration (calibrating across different feature channels). This segmentation allows each calibration dimension to be processed separately with dedicated calibration layers, reducing the overall computational complexity compared to calibrating the entire 3D feature space simultaneously.
Solution Approach 2:
The patent introduces calibration layers that operate in the feature space dimension (channel dimension) rather than directly in the spatiotemporal dimension. By calibrating features along the channel dimension using 1D convolutional operations, the patent avoids the cubic complexity of operating directly on the 3D spatiotemporal features, effectively transforming the problem to a lower-dimensional calibration space.
2Measurement precision
If 3D CNN is applied to video analysis, then inference accuracy is improved, but memory and compute demands become prohibitively high
Solution Approach 1:
The patent introduces calibration layers as intermediary components between the standard convolutional layers. These calibration layers act as mediators that adjust and normalize the 3D features extracted by the CNN, improving inference accuracy without requiring the main network to process all features at full complexity. The calibration layers operate on a compressed representation of the features, reducing overall computational demands.
Solution Approach 2:
The patent dynamically adjusts calibration parameters (such as scale and shift factors) based on the statistical properties of the input features. By changing these parameters adaptively rather than using fixed values, the system achieves higher inference accuracy while maintaining efficient computation through parameter-based transformations rather than complex structural modifications.
3Adaptability or versatility
If generalized 3D CNN architecture is used, then feature distribution variations are captured, but training speed decreases due to huge compute demands
Solution Approach 1:
The patent implements dynamic calibration where the calibration parameters are not fixed but are instead learned and adjusted during training based on the input feature distributions. This dynamic adaptation allows the model to capture feature distribution variations effectively while maintaining training efficiency, as the calibration operations are computationally lighter than retraining the entire 3D CNN architecture.
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, a network depth relay structure comprising a plurality of network depth calibration layers, where each network depth calibration layer is coupled to an output of a respective one of the plurality of convolution layers, and a feature dimension relay structure comprising a plurality of feature dimension calibration slices, where the feature dimension relay structure is coupled to an output of another layer of the plurality of convolution layers. Each network depth calibration layer is coupled to a preceding network depth calibration layer via first hidden state and cell state signals, and each feature dimension calibration slice is coupled to a preceding feature dimension calibration slice via second hidden state and cell state signals.