Transforming CNN Layers for Visual Sequence Learning
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Solution Overview
Problem
Existing methods for visual sequence learning, particularly with neural networks, fail to effectively leverage the representational power and generalization ability of pre-trained convolutional neural networks (CNNs) and struggle with addressing redundancy and diverse temporal dependencies in visual sequences.
Innovation Solution
The method involves transforming non-recurrent layers in a pre-trained CNN into recurrent layers, using feedforward weights as initial input-to-hidden weights and initializing hidden-to-hidden weights randomly, to create a visual sequence learning neural network model that processes video image data for classification or regression output.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional RNNs are used for visual sequence learning, then temporal dependencies can be captured, but the representational power and generalization ability of pre-trained CNNs cannot be effectively leveraged
Solution Approach 1:
The patent merges the strengths of CNNs and RNNs by integrating pre-trained CNN layers with recurrent layers. The CNN component extracts spatial features with high generalization ability from pre-training, while the RNN component captures temporal dependencies in video sequences. This combination allows the system to simultaneously leverage the representational power of pre-trained models and handle temporal relationships in visual sequences.
2Reliability
If pre-trained CNNs are used for feature extraction, then representational power is improved, but the model cannot effectively handle temporal contexts in video sequences
Solution Approach 1:
The patent introduces dynamic temporal processing by adding recurrent layers to the static pre-trained CNN architecture. The recurrent layers enable the model to dynamically process temporal contexts in video sequences by maintaining and updating hidden states across time steps, while the pre-trained CNN layers provide stable spatial feature extraction with high representational power.
3Adaptability or versatility
If RNNs are built on top of pre-trained CNNs, then temporal dependencies can be captured, but the number of parameters and computational complexity increase
Solution Approach 1:
The patent segments the neural network into distinct functional components: pre-trained CNN layers for spatial feature extraction and recurrent layers for temporal processing. This segmentation allows each component to specialize in its strength while maintaining a manageable overall complexity. The pre-trained CNN layers handle the computationally intensive feature extraction, while the recurrent layers focus on temporal dependencies with fewer parameters.
4Adaptability or versatility
If visual sequences are processed with standard RNN architectures, then temporal dependencies are captured, but redundancy in visual sequences cannot be effectively addressed
Solution Approach 1:
The patent extracts and removes redundancy from visual sequences by using the pre-trained CNN layers to identify and eliminate redundant spatial features before temporal processing. The recurrent layers then process the condensed temporal information, effectively addressing redundancy in visual sequences while maintaining the ability to capture diverse temporal dependencies.
Data Source
AI summary
A method, computer readable medium, and system are disclosed for visual sequence learning using neural networks. The method includes the steps of replacing a non-recurrent layer within a trained convolutional neural network model with a recurrent layer to produce a visual sequence learning neural network model and transforming feedforward weights for the non-recurrent layer into input-to-hidden weights of the recurrent layer to produce a transformed recurrent layer. The method also includes the steps of setting hidden-to-hidden weights of the recurrent layer to initial values and processing video image data by the visual sequence learning neural network model to generate classification or regression output data.