Weight mechanism adding a constant in concatenation of series connect

by   Xiaojie Qi, et al.

It is a consensus that feature maps in the shallow layer are more related to image attributes such as texture and shape, whereas abstract semantic representation exists in the deep layer. Meanwhile, some image information will be lost in the process of the convolution operation. Naturally, the direct method is combining them together to gain lost detailed information through concatenation or adding. In fact, the image representation flowed in feature fusion can not match with the semantic representation completely, and the semantic deviation in different layers also destroy the information purification, that leads to useless information being mixed into the fusion layers. Therefore, it is crucial to narrow the gap among the fused layers and reduce the impact of noises during fusion. In this paper, we propose a method named weight mechanism to reduce the gap between feature maps in concatenation of series connection, and we get a better result of 0.80 Massachusetts building dataset by changing the weight of the concatenation of series connection in residual U-Net. Specifically, we design a new architecture named fused U-Net to test weight mechanism, and it also gains 0.12 improvement.


page 1

page 2

page 3

page 4


When Image Decomposition Meets Deep Learning: A Novel Infrared and Visible Image Fusion Method

Infrared and visible image fusion, as a hot topic in image processing an...

Image fusion using symmetric skip autoencodervia an Adversarial Regulariser

It is a challenging task to extract the best of both worlds by combining...

Atrous Convolutional Neural Network (ACNN) for Biomedical Semantic Segmentation with Dimensionally Lossless Feature Maps

Deep Convolutional Neural Networks (DCNNs) are showing impressive perfor...

SFPN: Synthetic FPN for Object Detection

FPN (Feature Pyramid Network) has become a basic component of most SoTA ...

SkipcrossNets: Adaptive Skip-cross Fusion for Road Detection

Multi-modal fusion is increasingly being used for autonomous driving tas...

Dynamic Feature Fusion for Semantic Edge Detection

Features from multiple scales can greatly benefit the semantic edge dete...

Similarity network fusion for scholarly journals

This paper explores intellectual and social proximity among scholarly jo...

Please sign up or login with your details

Forgot password? Click here to reset