Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Novel Transformer Based Semantic Segmentation Scheme for
  Fine-Resolution Remote Sensing Images   The fully convolutional network (FCN) with an encoder-decoder architecture
has been the standard paradigm for semantic segmentation. The encoder-decoder
architecture utilizes an encoder to capture multilevel feature maps, which are
incorporated into the final prediction by a decoder. As the context is crucial
for precise segmentation, tremendous effort has been made to extract such
information in an intelligent fashion, including employing dilated/atrous
convolutions or inserting attention modules. However, these endeavors are all
based on the FCN architecture with ResNet or other backbones, which cannot
fully exploit the context from the theoretical concept. By contrast, we
introduce the Swin Transformer as the backbone to extract the context
information and design a novel decoder of densely connected feature aggregation
module (DCFAM) to restore the resolution and produce the segmentation map. The
experimental results on two remotely sensed semantic segmentation datasets
demonstrate the effectiveness of the proposed scheme.

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The categories of this paper are: q-fin
34
The categories of this paper are: chem-ph
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The categories of this paper are: adap-org
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The categories of this paper are: eess
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
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The categories of this paper are: chao-dyn
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The categories of this paper are: nucl-ex
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The categories of this paper are: solv-int
2
The categories of this paper are: math
31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph
35
The categories of this paper are: mtrl-th
6
The categories of this paper are: astro-ph
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The categories of this paper are: nlin
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The categories of this paper are: hep-ph