Poly-Scale Kernel-Wise Convolution for Scale-Sensitive CNNs
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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 such as 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 model complexity increase significantly
Solution Approach 1:
The invention 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 applies a specific dilation rate, allowing multi-scale feature fusion without requiring entirely new CNN architectures or increasing computational budget significantly.
Solution Approach 2:
The invention changes the dilation rate parameter of convolutional kernels to enable multi-scale feature extraction. By varying the dilation rate across different kernel groups while maintaining the same kernel size and computational resources, the model achieves scale-invariance without increasing computational complexity.
2Adaptability or versatility
If multiple sets of depth-wise convolutions with different kernel sizes are used to increase network tolerance to spatial geometric transformations, then scale-invariance is improved, but parameters and computational budget increase
Solution Approach 1:
The invention makes each convolutional kernel multi-functional by enabling it to capture features at multiple scales simultaneously through different dilation rates. This universal approach allows a single kernel structure to perform the function of multiple separate convolutional layers with different kernel sizes, reducing the total number of parameters while maintaining adaptability to spatial geometric transformations.
3Device complexity
If fixed-sized receptive fields are used in streamlined CNN backbones, then model simplicity is maintained, but ability to gather diverse information from objects of various sizes is limited
Solution Approach 1:
The invention introduces dynamic receptive field sizes within a fixed kernel structure by varying the dilation rate. This allows the effective receptive field to adapt dynamically based on the dilation rate assigned to each kernel group, enabling the model to gather diverse information from objects of various sizes while maintaining the simplicity of a unified kernel architecture.
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.