Poly-Scale Kernel-Wise Convolution for Multi-Scale CNN Recognition
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Solution Overview
Problem
Modern convolutional neural networks (CNNs) are scale-sensitive due to fixed-sized receptive fields, limiting their performance on complex visual recognition tasks like large-scale image classification and object detection, and existing multi-scale feature fusion techniques increase computational costs and require new architectures.
Innovation Solution
Implement poly-scale kernel-wise CNN layers with varying dilation rates for filter kernels to capture diverse information at different scales without increasing computational resources, using a cyclic pattern of dilation rates across filters to achieve multi-scale feature fusion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multi-scale feature fusion techniques (dense skip connections, inception parallel layers) are applied to address scale-sensitivity, then CNN performance on visual recognition tasks is improved, but computational budget and parameters increase
Solution Approach 1:
The patent segments the convolutional kernel into multiple sub-kernels, each responsible for capturing features at different scales. This is achieved by dividing the kernel into groups where each group processes specific spatial regions, allowing multi-scale feature extraction without requiring entirely new CNN architectures or increasing computational budget significantly.
Solution Approach 2:
The patent introduces a new dimension to the convolutional kernel structure by applying poly-scale dilation rates. Instead of using traditional single-scale kernels, the invention creates a multi-dimensional kernel structure that simultaneously captures features at multiple scales through controlled dilation, thereby improving performance without proportionally increasing computational cost.
2Device complexity
If fixed-sized receptive fields are used in streamlined CNN backbones, then network architecture is simplified, but ability to gather diverse information from objects of various sizes is reduced
Solution Approach 1:
The patent makes the receptive field dynamic by introducing variable dilation rates that can adapt to different scales. The poly-scale dilation mechanism allows the network to dynamically adjust its receptive field size and configuration based on the input data, enabling the simplified architecture to handle objects of various sizes effectively.
Solution Approach 2:
The patent changes the kernel parameters by introducing poly-scale dilation rates that vary across different spatial positions. This parameter modification allows the fixed architecture to achieve variable receptive fields, thereby maintaining both architectural simplicity and the ability to gather diverse information from objects of various sizes.
3Reliability
If multiple sets of depth-wise convolutions with different kernel sizes are used to increase network tolerance to spatial geometric transformations, then scale sensitivity is addressed, but computational cost and architecture complexity increase
Solution Approach 1:
The patent merges multiple depth-wise convolutions with different kernel sizes into a single unified convolutional layer using poly-scale dilation. Instead of implementing separate convolutional layers for different scales, the invention combines them into one layer that simultaneously performs multi-scale feature extraction through controlled dilation patterns, thereby reducing architecture complexity and computational cost while maintaining tolerance to spatial geometric transformations.
Data Source
AI summary
Techniques related to poly-scale kernel-wise convolutional neural network layers are discussed. A poly-scale kernel-wise convolutional neural network layer is applied to an input volume to generate an output volume and include filters each having a number of filter kernels with the same sample rate and differing dilation rates optionally in a repeating pattern of dilation rate groups within each of filters with the pattern of dilation rate groups offset between the filters the poly-scale kernel-wise convolutional neural network layer.