Unable to understand the meaning of following lines of the research paper for image segmentation
Generally, if you look at image segmentation models, they have two main paths, what the author of your paper calls encoder and decoder paths. 

The role of the encoder is to contract the size of the image while extracting meaningful information, while the decoder restores the contracted image to its original dimensions. However, a lot of information is lost during contraction. To overcome this issue the decoder takes some features directly from the contraction path via skip connections. 

A certain decoder layer's input is actually the concatenation of two things:


The output of its previous layer
The output of the respective layer (the one with the same dimensions) of the encoder


This becomes more clear if you look at the following image:



The left part of the network is the encoder and the right is the decoder. The skip connections are the grey horizontal lines that go directly from the encoder to the decoder.

These can't appear the way you portray the model because you show only the layers and not any information about how they are connected. I think you assume the network should be sequential, but segmentation models usually aren't.