Building a Multi-Scale Convolutional Neural Network with Context

Overview of Multi-Scale CNNs

Motivation for Multi-Scale Representations

Convolutional neural networks (CNNs) typically use a fixed receptive field size, which limits their ability to capture features at multiple scales. However, many computer vision tasks, such as object detection and image super-resolution, require the ability to process information at different scales. Multi-scale CNNs address this by incorporating multiple convolutional kernels of varying sizes to extract features at multiple scales. (Du et al., 2018), (Jiang et al., 2021)

Approaches to Multi-Scale CNNs

There are a few common approaches to building multi-scale CNNs:

1. Late Fusion: Combine feature maps from different scales at the end of the network. (Tong et al., 2019)
2. Feature Pyramid Networks: Use a top-down pathway with lateral connections to build a feature pyramid, allowing the network to access multi-scale features. (Tong et al., 2019)
3. Multi-Scale Competitive Modules: Use a module with multiple convolutional kernels of different sizes, along with a competitive activation function to select the optimal scale. (Du et al., 2018)

Incorporating Context Information

In addition to multi-scale features, incorporating context information can also improve the performance of CNNs. Context information can be obtained from lower or higher resolution versions of the input image, or from surrounding regions of the image. Some approaches to incorporating context include:

1. Concatenating Feature Maps: Concatenate feature maps from different scales or resolutions to combine local and global information. (Tong et al., 2019)
2. Atrous Convolution: Use atrous (dilated) convolution to expand the receptive field and capture more context without increasing the number of parameters. (Jiang et al., 2021)
3. Parallel Multi-Scale Spatial Pooling: Use a parallel set of pooling layers with different kernel sizes to extract multi-scale spatial context. (Chen et al., 2020)