Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Attention on Classification for Fire Segmentation   Detection and localization of fire in images and videos are important in
tackling fire incidents. Although semantic segmentation methods can be used to
indicate the location of pixels with fire in the images, their predictions are
localized, and they often fail to consider global information of the existence
of fire in the image which is implicit in the image labels. We propose a
Convolutional Neural Network (CNN) for joint classification and segmentation of
fire in images which improves the performance of the fire segmentation. We use
a spatial self-attention mechanism to capture long-range dependency between
pixels, and a new channel attention module which uses the classification
probability as an attention weight. The network is jointly trained for both
segmentation and classification, leading to improvement in the performance of
the single-task image segmentation methods, and the previous methods proposed
for fire segmentation.

12
The categories of this paper are: nucl-th
31
The categories of this paper are: ao-sci
35
The categories of this paper are: mtrl-th
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The categories of this paper are: nucl-ex
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The categories of this paper are: q-fin
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The categories of this paper are: eess
26
The categories of this paper are: bayes-an
33
The categories of this paper are: plasm-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: solv-int
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The categories of this paper are: cond-mat
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The categories of this paper are: hep-ph
36
The categories of this paper are: cmp-lg
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The categories of this paper are: quant-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: alg-geom