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(WIP) Feature/ieee format output #112
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9f89684
Adding some new Makefile targets
9e34674
fix errors make/index.pdf errors: 'Paragraph ended before' and 'Missi…
6a4bd16
WIP attempting to output to PDF, specifically IEEE. Undefined refs.
d9b19ed
a bit of Makefile cleanup
cd0e8b5
remove docker-related items
5d272a7
CDL: a bit more cleanup
326f4f5
CDL: finally got .tex compiled to PDF with citations
e56984f
CDL: comments and typo fix
f4c8946
CDL: add --bilbiography= to pandoc args
76e1446
CDL: halfway-functional IEEE output, undefined control sequence subpa…
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Original file line number | Diff line number | Diff line change |
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@@ -0,0 +1,4 @@ | ||
FROM pandoc/extra | ||
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RUN tlmgr install pdflscape | ||
RUN tlmgr install twemoji-colr | ||
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Original file line number | Diff line number | Diff line change |
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@@ -8,6 +8,7 @@ secnumdepth: 3 | |
header-includes: | ||
- | | ||
\usepackage{pdflscape} | ||
\usepackage{graphicx} | ||
author: | ||
- Amit Moryossef ([[email protected]](mailto:[email protected])) | ||
- Yoav Goldberg ([[email protected]](mailto:[email protected])) | ||
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@@ -819,8 +820,8 @@ or aligned to the spoken language captions, and showed their DTW is advantageous | |
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@xiao2020skeleton closed the loop by proposing a text-to-pose-to-text model for the case of isolated sign language recognition. | ||
They first trained a classifier to take a sequence of poses encoded by a BiLSTM and classify the relevant sign, then proposed a production system to take a single sign and sample a constant length sequence of 50 poses from a Gaussian Mixture Model. | ||
These components are combined such that given a sign class $y$, a pose sequence is generated, then classified back into a sign class $ŷ$, | ||
and the loss is applied between $y$ and $ŷ$, and not directly on the generated pose sequence. | ||
These components are combined such that given a sign class $y$, a pose sequence is generated, then classified back into a sign class $\hat{y}$, | ||
and the loss is applied between $y$ and $\hat{y}$, and not directly on the generated pose sequence. | ||
They evaluate their approach on the CSL dataset [@dataset:huang2018video] and show that their generated pose sequences | ||
almost reach the same classification performance as the reference sequences. | ||
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