Convolutional Neural Network with Recurrent Connections for Video Frame Prediction
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Solution Overview
Problem
Current deep learning technologies face challenges in achieving high-level semantic analysis, particularly in video data, due to the reliance on local information rather than global context and the difficulty in end-to-end modeling and prediction using deep neural networks for pixel-level classification in video data.
Innovation Solution
A method involving a convolutional neural network with recurrent connections between convolutional and de-convolutional layers, utilizing Long Short-Term Memory networks, to perform feature extraction and generate reconstructed media content, including predicted future video frames, and utilizing a discriminator system to determine the authenticity of the reconstructed content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If convolutional neural networks use local information for prediction, then manufacturing precision is improved, but loss of information increases
Solution Approach 1:
The patent merges local and global information by combining convolutional neural networks with recurrent neural networks. The CNN extracts local features while the RNN aggregates global context from the entire video sequence, and their outputs are combined to produce predictions that benefit from both local precision and global understanding.
Solution Approach 2:
The patent adds a temporal dimension to the traditional spatial processing of CNNs by introducing recurrent connections that process information across time steps. This allows the model to capture temporal dependencies and global context while maintaining the local feature extraction capabilities of convolutional layers.
2Manufacturing precision
If deep neural networks model image data, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the network into distinct functional modules: a CNN component for local feature extraction, an RNN component for temporal modeling, and a prediction component. This modular segmentation makes the complex system more manageable and allows each component to specialize in specific tasks.
Solution Approach 2:
The patent introduces dynamic recurrent connections that adaptively process temporal information, allowing the network to adjust its behavior based on the temporal patterns in video data. This dynamic approach enables the model to handle varying temporal dependencies without requiring a completely different architecture for each scenario.
3Loss of information
If recurrent connections are added between convolutional and de-convolutional layers, then loss of information is reduced, but device complexity increases
Solution Approach 1:
The patent introduces recurrent neural network layers as intermediary components between the CNN and de-CNN modules. These RNN layers act as mediators that process and transmit temporal information across the encoder-decoder architecture, enabling information flow across time steps without directly complicating the convolutional operations.
Data Source
AI summary
The invention relates to a method, an apparatus and a computer program product for analyzing media content. The method comprises receiving media content; performing feature extraction of the media content at a plurality of convolution layers to produce a plurality of layer-specific feature maps; transmitting from the plurality of convolution layers a corresponding layer-specific feature map to a corresponding de-convolution layer of a plurality of de-convolution layers via a recurrent connection between the plurality of convolution layers and the plurality of de-convolution layers; and generating a reconstructed media content based on the plurality of feature maps.