Skip Architecture Neural Network for Semantic Segmentation
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Solution Overview
Problem
Current image recognition and computer vision technologies face challenges in efficiently and accurately processing crowded, finely detailed, and complex images, such as floorplans, due to high computational costs and limitations in handling variations in scale, orientation, and resolution.
Innovation Solution
A convolutional neural network (CNN) system with a self-adaptive architecture that skips unnecessary processing layers, allowing for end-to-end, pixel-to-pixel training and semantic segmentation, reducing computational costs by determining the influence of pooling layers and optimizing the network architecture for improved efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional convolutional neural networks process all pooling layers, then comprehensive feature extraction is achieved, but computational cost increases significantly
Solution Approach 1:
The patent applies dynamics by making the pooling layer architecture adaptive rather than static. The network dynamically determines which pooling layers to skip based on the specific input image characteristics, allowing the architecture to flex between comprehensive processing and efficient processing modes. This is achieved through trainable parameters that control the activation of different pooling layers during inference.
Solution Approach 2:
The patent changes parameters by introducing learnable parameters that control the skipping behavior of pooling layers. These parameters are trained alongside the main network weights, allowing the system to learn optimal processing paths for different types of images. The parameter changes enable the network to adapt its computational depth based on input complexity.
2Productivity
If pooling layers are skipped to reduce computation, then processing efficiency improves, but feature extraction completeness may deteriorate
Solution Approach 1:
The patent implements feedback mechanisms where the network's performance on validation data guides the learning of skip architecture parameters. The feedback loop allows the system to learn which pooling layers are critical for maintaining feature extraction completeness while skipping others to improve efficiency. This feedback-driven approach ensures that efficiency gains do not come at the cost of essential feature extraction.
Solution Approach 2:
The network performs self-service by automatically determining its own optimal architecture for each input type through the learned skip parameters. Rather than requiring manual architecture design or external optimization, the network self-adapts its processing path based on the input characteristics, making the efficiency-feature extraction tradeoff automatically.
3Ease of manufacture
If fixed architecture is used, then implementation is simpler, but adaptability to different image complexities is reduced
Solution Approach 1:
The patent applies segmentation by dividing the pooling layer architecture into independently controllable segments. Each pooling layer can be individually skipped or activated based on learned parameters, allowing the network to segment its processing path according to input requirements. This segmentation maintains implementation simplicity while enabling adaptability to different image complexities.
4Measurement precision
If more processing layers are added to handle fine details, then segmentation accuracy improves, but computational resources required increase
Solution Approach 1:
The patent applies partial action by selectively activating only the necessary pooling layers based on input image characteristics. Rather than always applying the full sequence of processing layers, the network performs partial processing by skipping redundant layers for simple inputs while maintaining full processing depth for complex inputs requiring fine detail segmentation. This reduces average computational resources while maintaining peak accuracy when needed.
Data Source
AI summary
A method of using a computer to semantically segment an image using a convolutional neural network system where a processor configured to convolve an input image with a plurality of filters and outputting a first output volume, pool the first output volume and creating a first activation map, determine the level of influence of the first activation map on the semantic segmentation, up-pool the first activation map to form an output image having a same number of pixels as the input image, output a probabilistic segmentation result, labeling each pixel's probability that it is a particular label, and the determination of the level of influence of the first activation map on the semantic segmentation is done using a gate layer that is positioned between a pooling layer and an up-pooling layer.