Omni-scale convolution for CNN scale variance
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Solution Overview
Problem
Conventional convolutional neural networks (CNNs) are scale-sensitive due to fixed-sized receptive fields, limiting their ability to gather diverse information from objects of various sizes and understand contextual backgrounds, which restricts their performance in complex visual recognition tasks.
Innovation Solution
The implementation of omni-scale convolution in CNNs, where constituent kernels of a filter use a group of dilation rates to extract features corresponding to different receptive fields, and the group of dilation rates alternates cyclically along input and output channels, enabling the extraction of diverse scale information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional CNNs with fixed-sized receptive fields are used, then the network structure is simple and computational cost is low, but the ability to gather diverse information from objects of various sizes is limited
Solution Approach 1:
The patent applies dynamics by making the receptive field size dynamic rather than fixed. Each convolutional filter dynamically adapts its receptive field size based on the scale of objects in the input image through learned scaling factors, allowing the network to automatically adjust to various object sizes without manual intervention or complex multi-scale architectures.
Solution Approach 2:
The patent changes the parameter of receptive field size from a fixed value to a learnable parameter. By introducing scaling factors that are learned during training, the receptive field size becomes a variable parameter that can be optimized for different objects, resolving the contradiction between simplicity and adaptability.
2Reliability
If conventional CNNs with fixed-sized receptive fields are used, then the network structure is simple, but performance in complex visual recognition tasks is limited
Solution Approach 1:
The dynamic receptive field mechanism allows the network to adapt to complex visual recognition tasks by adjusting receptive field sizes according to object scales, improving reliability without requiring complex multi-scale processing architectures or multiple parallel networks.
Solution Approach 2:
The patent makes a single convolutional layer universal by enabling it to handle multiple scale ranges through learned scaling factors. This single layer can process both small and large objects effectively, replacing the need for multiple specialized layers or networks, thus improving performance without proportionally increasing complexity.
3Reliability
If multi-scale processing is added to improve scale variance robustness, then robustness to scale variance improves, but computational cost increases
Solution Approach 1:
The patent merges multi-scale processing capabilities into a single convolutional layer by combining multiple receptive field sizes within one layer through learned scaling factors. This eliminates the need for separate processing paths for different scales, achieving robustness to scale variance without the computational overhead of multiple parallel processing streams.
Solution Approach 2:
By dynamically selecting and adjusting receptive field sizes based on input characteristics rather than processing all scales simultaneously, the network achieves scale variance robustness only when needed, reducing unnecessary computational cost compared to static multi-scale architectures that process all scales regardless of input requirements.
Data Source
AI summary
Omni-scale convolution for convolutional neural networks is disclosed. An example of an apparatus includes one or more processors to process data, including processing for a convolutional neural network (CNN); and a memory to store data, including CNN data, wherein processing of input data by the CNN includes implementing omni-scale convolution in one or more convolutional layers of the CNN, implementation of the omni-scale convolution into a convolutional layer of the one or more convolutional layers including at least applying multiple dilation rates in a plurality of kernels of a kernel lattice of the convolutional layer, and applying a cyclic pattern for the multiple dilation rates in the plurality of kernels of the convolutional layer.