Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Per-Pixel Feedback for improving Semantic Segmentation   Semantic segmentation is the task of assigning a label to each pixel in the
image.In recent years, deep convolutional neural networks have been driving
advances in multiple tasks related to cognition. Although, DCNNs have resulted
in unprecedented visual recognition performances, they offer little
transparency. To understand how DCNN based models work at the task of semantic
segmentation, we try to analyze the DCNN models in semantic segmentation. We
try to find the importance of global image information for labeling pixels.
  Based on the experiments on discriminative regions, and modeling of
fixations, we propose a set of new training loss functions for fine-tuning DCNN
based models. The proposed training regime has shown improvement in performance
of DeepLab Large FOV(VGG-16) Segmentation model for PASCAL VOC 2012 dataset.
However, further test remains to conclusively evaluate the benefits due to the
proposed loss functions across models, and data-sets.
  Submitted in part fulfillment of the requirements for the degree of
Integrated Masters of Science in Applied Mathematics.
  Update: Further Experiment showed minimal benefits.
  Code Available [here](https://github.com/BardOfCodes/Seg-Unravel).

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The categories of this paper are: acc-phys
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The categories of this paper are: q-bio
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The categories of this paper are: alg-geom
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The categories of this paper are: hep-lat
0
The categories of this paper are: hep-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: eess
2
The categories of this paper are: math
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The categories of this paper are: stat
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The categories of this paper are: quant-ph
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The categories of this paper are: econ
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The categories of this paper are: solv-int
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The categories of this paper are: supr-con
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The categories of this paper are: cond-mat
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The categories of this paper are: chao-dyn
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The categories of this paper are: dg-ga