Hierarchical Feature Fusion (HFF): An Effective Method for Image Model Blocks
What is Hierarchical Feature Fusion?
Hierarchical Feature Fusion (HFF) is a method of fusing feature maps obtained by convolving an image with different dilation rates. It is used in image model blocks like ESP and EESP to eliminate unwanted artifacts caused by a large receptive field introduced by dilated convolutions.
How does Hierarchical Feature Fusion work?
The ESP (Efficient Spatial Pyramid) module uses dilated convolutions to obtain a large receptive field. However, this introduces an unwanted checkerboard or gridding artifact in the feature maps, which affects the image quality. HFF solves this problem by hierarchically combining feature maps obtained using kernels of different dilation rates. This approach eliminates the gridding artifact, resulting in high-quality images.
Why is Hierarchical Feature Fusion important?
Hierarchical Feature Fusion is important because it enhances the performance of image model blocks like ESP and EESP. By eliminating the gridding artifact introduced by dilated convolutions, HFF improves the accuracy of image recognition systems. It also improves the visual quality of images by reducing the appearance of noise and distortions.
How is Hierarchical Feature Fusion implemented?
HFF is implemented by combining feature maps obtained from convolutions with different dilation rates in a hierarchical manner. The feature maps are hierarchically combined by adding them together before they are concatenated. This approach ensures that the final output feature map has information from all the different dilation rates and reduces the gridding artifact.
For example, in an ESP module, feature maps obtained from dilated convolutions with dilation rates of 1, 2, 4, and 8 are combined hierarchically before being concatenated. The feature maps obtained from the dilated convolutions at a specific layer are then combined with the output feature map of the previous layer, resulting in an HFF block that reduces the gridding artifact and improves the overall performance of the model.
What are the benefits of Hierarchical Feature Fusion?
Hierarchical Feature Fusion has several benefits, some of which include:
- Elimination of checkerboard or gridding artifacts introduced by dilated convolutions
- Improvement in model accuracy by reducing noise and distortions in image feature maps
- Higher receptive fields without increasing complexity of the ESP or EESP model blocks
- Better visual quality of images used in image recognition systems
Hierarchical Feature Fusion is a method of fusing feature maps obtained by convolving an image with different dilation rates that solves the problem of gridding artifact introduced by dilated convolutions. It is an important technique used in image model blocks like ESP and EESP that improves image quality, reduces noise and distortions, and improves the accuracy of image recognition systems.
