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Adaptive feature recombination and recalibration for semantic segmentation with Fully Convolutional Networks by Pereira, Sergio; Pinto, Adriano; Amorim, Joana; Ribeiro, Alexandrine; Alves, Victor; Silva, Carlos A. is a scholarly article available to read on EtoBox.

What is Adaptive feature recombination and recalibration for semantic segmentation with Fully Convolutional Networks about?

Fully Convolutional Networks have been achieving remarkable results in image semantic segmentation, while being efficient. Such efficiency results from the capability of segmenting several voxels in a single forward pass. So, there is a direct spatial correspondence between a unit in a feature map and the voxel in the same location. In a convolutional layer, the kernel spans over all channels and extracts information from them. We observe that linear recombination of feature maps by increasing the number of channels followed by compression may enhance their discriminative power. Moreover, not all feature maps have the same relevance for the classes being predicted. In order to learn the inter-channel relationships and recalibrate the channels to suppress the less relevant ones, Squeeze and Excitation blocks were proposed in the context of image classification with Convolutional Neural Networks. However, this is not well adapted for segmentation with Fully Convolutional Networks since they segment several objects simultaneously, hence a feature map may contain relevant information only in some locations. In this paper, we propose recombination of features and a spatially adaptive re

Author

Pereira, Sergio; Pinto, Adriano; Amorim, Joana; Ribeiro, Alexandrine; Alves, Victor; Silva, Carlos A.

Published

2020

Language

EN