Paper Writing with LaTeX
PDF: https://filebox.ece.vt.edu/~jbhuang/slides/Research%20101%20-%20Paper%20Writing%20with%20LaTeX.pdf
PPTX: https://filebox.ece.vt.edu/~jbhuang/slides/Research%20101%20-%20Paper%20Writing%20with%20LaTeX.pptx
This slide is used to do an introduction for the matplotlib library and this will be a very basic introduction. As matplotlib is a very used and famous library for machine learning this will be very helpful to teach a student with no coding background and they can start the plotting of maps from the ending of the slide by there own.
This document for reference material to SPPU course on Latex. Latex is a universally used software for preparing quality documents. It is not a word processor. This document has been compiled by taking examples and references from various texts available on the subject. It is not meant to serve as beginner's guide to latex. History, Features and Applications of Latex along with basic Latex features such as how to form tables, how to write equations, how to enumerate data items etc are discussed.
This slide is used to do an introduction for the matplotlib library and this will be a very basic introduction. As matplotlib is a very used and famous library for machine learning this will be very helpful to teach a student with no coding background and they can start the plotting of maps from the ending of the slide by there own.
This document for reference material to SPPU course on Latex. Latex is a universally used software for preparing quality documents. It is not a word processor. This document has been compiled by taking examples and references from various texts available on the subject. It is not meant to serve as beginner's guide to latex. History, Features and Applications of Latex along with basic Latex features such as how to form tables, how to write equations, how to enumerate data items etc are discussed.
Introduction to latex. Write your first document in latex
To Download latex for windows (163 MB)
http://miktex.org/download
To Download texmaker editor (53 MB)
http://www.xm1math.net/texmaker/download.html#windows
Here you can find a good latex templates
http://www.latextemplates.com/
https://www.sharelatex.com/templates/
https://www.writelatex.com/templates
Try sharelatex or writelatex for online editing
https://www.sharelatex.com/
https://www.writelatex.com/
An introduction to plotting in Python landscape. Mostly matplotlib, but also peek at other packages. Jupyter notebook with examples: https://gist.github.com/bzamecnik/b58579e319287abcb3ca
Talk held on 2015-01-07 at the Python for Data Science workshop organized by Jumpshot & AVAST.
This is the basic introduction of the pandas library, you can use it for teaching this library for machine learning introduction. This slide will be able to help to understand the basics of pandas to the students with no coding background.
Reading academic papers is one of the most important parts of scientific research. However, junior graduate students may spend a lot of time learning how to read papers efficiently and effectively. In this talk, I will discuss some basic issues and introduce useful websites/tools/tips for paper reading.
General principles and tricks for writing fast MATLAB code.
Powerpoint slides: https://uofi.box.com/shared/static/yg4ry6s1c9qamsvk6sk7cdbzbmn2z7b8.pptx
Introduction to latex. Write your first document in latex
To Download latex for windows (163 MB)
http://miktex.org/download
To Download texmaker editor (53 MB)
http://www.xm1math.net/texmaker/download.html#windows
Here you can find a good latex templates
http://www.latextemplates.com/
https://www.sharelatex.com/templates/
https://www.writelatex.com/templates
Try sharelatex or writelatex for online editing
https://www.sharelatex.com/
https://www.writelatex.com/
An introduction to plotting in Python landscape. Mostly matplotlib, but also peek at other packages. Jupyter notebook with examples: https://gist.github.com/bzamecnik/b58579e319287abcb3ca
Talk held on 2015-01-07 at the Python for Data Science workshop organized by Jumpshot & AVAST.
This is the basic introduction of the pandas library, you can use it for teaching this library for machine learning introduction. This slide will be able to help to understand the basics of pandas to the students with no coding background.
Reading academic papers is one of the most important parts of scientific research. However, junior graduate students may spend a lot of time learning how to read papers efficiently and effectively. In this talk, I will discuss some basic issues and introduce useful websites/tools/tips for paper reading.
General principles and tricks for writing fast MATLAB code.
Powerpoint slides: https://uofi.box.com/shared/static/yg4ry6s1c9qamsvk6sk7cdbzbmn2z7b8.pptx
Computer vision techniques can be seen in various aspects in our daily life with tremendous impacts. This slides aim at introducing basic concepts of computer vision and applications for the general public.
Download link: https://uofi.box.com/shared/static/24vy7aule67o4g6djr83hzurf5a9lfp6.pptx
Computer vision has been studied for more than 40 years. Due to the increasingly diverse and rapidly developed topics in vision and the related fields (e.g., machine learning, signal processing, cognitive science), the tasks to come up with new research ideas are usually daunting for junior graduate students in this field. In this talk, I will present five methods to come up with new research ideas. For each method, I will give several examples (i.e., existing works in the literature) to illustrate how the method works in practice.
This is a common sense talk and will not have complicated math equations and theories.
Note: The content of this talk is inspired by "Raskar Idea Hexagon" - Prof. Ramesh Raskar's talk on "How to come up with new Ideas".
To download the presentation slide with videos, please visit
http://jbhuang0604.blogspot.com/2010/05/how-to-come-up-with-new-research-ideas.html
For the video lecture (in Chinese), please visit
http://jbhuang0604.blogspot.com/2010/06/blog-post_14.html
What makes a creative photograph? This talk summarizes five approaches to make creative photographs. For each approach, many example images from the internet are used to demonstrate how the method works in practice.
For more explanations on example images, please visit my blog: http://jbhuang0604.blogspot.com/
Enhancing Color Representation for the Color Vision Impaired (CVAVI 2008)Jia-Bin Huang
In this paper, we propose a fast re-coloring algorithm to improve the accessibility for the color vision impaired. Compared to people with normal color vision, people with color vision impairment have difficulty in distinguishing between certain combinations of colors. This may hinder visual communication owing to the increasing use of colors in recent years. To address this problem, we re-map the hue components in the HSV color space based on the statistics of local characteristics of the original color image. We enhance the color contrast through generalized histogram equalization. A control parameter is provided for various users to specify the degree of enhancement to meet their needs. Experimental results are illustrated to demonstrate the effectiveness and efficiency of the proposed re-coloring algorithm.
In this paper, we describe a new interactive image completion system that allows users to easily specify various forms of mid-level structures in the image. Our system supports the specification of four basic symmetric types: reflection, translation, rotation, and glide. The user inputs are automatically converted into guidance maps that encode
possible candidate shifts and, indirectly, local transformations of rotation and scale. These guidance maps are used in conjunction with a color matching cost for image
completion. We show that our system is capable of handling a variety of challenging examples.
http://www.jiabinhuang.com/
Toward Accurate and Robust Cross-Ratio based Gaze Trackers Through Learning F...Jia-Bin Huang
Jia-Bin Huang, Qin Cai, Zicheng Liu, Narendra Ahuja, and Zhengyou Zhang
Towards Accurate and Robust Cross-Ratio based Gaze Trackers Through Learning From Simulation
Proceedings of ACM Symposium on Eye Tracking Research & Applications (ETRA), 2014
ETRA 2014 Best Paper Award
Saliency Detection via Divergence Analysis: A Unified Perspective ICPR 2012Jia-Bin Huang
A number of bottom-up saliency detection algorithms have been proposed in the literature. Since these have been developed from intuition and principles inspired by psychophysical studies of human vision, the theoretical relations among them are unclear. In this paper, we present a unifying perspective. Saliency of an image area is defined in terms of divergence between certain feature distributions estimated from the
central part and its surround. We show that various, seemingly different saliency estimation algorithms are in fact closely related. We also discuss some commonly
used center-surround selection strategies. Experiments with two datasets are presented to quantify the relative advantages of these algorithms.
Best student paper award in Computer Vision and Robotics Track
Image Completion using Planar Structure Guidance (SIGGRAPH 2014)Jia-Bin Huang
We propose a method for automatically guiding patch-based image completion using mid-level structural cues. Our method first estimates planar projection parameters, softly segments the known region into planes, and discovers translational regularity within these planes. This information is then converted into soft constraints for the low-level completion algorithm by defining prior probabilities for patch offsets and transformations. Our method handles multiple planes, and in the absence of any detected planes falls back to a baseline fronto-parallel image completion algorithm. We validate our technique through extensive comparisons with state-of-the-art algorithms on a variety of scenes.
Project page: https://sites.google.com/site/jbhuang0604/publications/struct_completion
Single Image Super-Resolution from Transformed Self-Exemplars (CVPR 2015)Jia-Bin Huang
Self-similarity based super-resolution (SR) algorithms are able to produce visually pleasing results without extensive training on external databases. Such algorithms exploit the statistical prior that patches in a natural image tend to recur within and across scales of the same image. However, the internal dictionary obtained from the given image may not always be sufficiently expressive to cover the textural appearance variations in the scene. In this paper, we extend self-similarity based SR to overcome this drawback. We expand the internal patch search space by allowing geometric variations. We do so by explicitly localizing planes in the scene and using the detected perspective geometry to guide the patch search process. We also incorporate additional affine transformations to accommodate local shape variations. We propose a compositional model to simultaneously handle both types of transformations. We extensively evaluate the performance in both urban and natural scenes. Even without using any external training databases, we achieve significantly superior results on urban scenes, while maintaining comparable performance on natural scenes as other state-of-the-art SR algorithms.
http://bit.ly/selfexemplarsr
You'll get everything you need to get started with programming in MATLAB. What you will learn:
-Matrices and Vectors
-2-D Plotting
-User-defined functions
-Logical statements: if-elseif, switch-case
-Looping techniques: for and while loops
-Plus more!
Here is my updated CV using the ModernCV template (http://www.latextemplates.com/template/moderncv-cv-and-cover-letter).
You can find the Tex source file in (https://dl.dropbox.com/u/2810224/Homepage/resume/modern%20style.rar)
Estimating Human Pose from Occluded Images (ACCV 2009)Jia-Bin Huang
We address the problem of recovering 3D human pose from single 2D images, in which the pose estimation problem is formulated as a direct nonlinear regression from image observation to 3D joint positions. One key issue that has not been addressed in the literature is how to estimate 3D pose when humans in the scenes are partially or heavily occluded. When occlusions occur, features extracted from image observations (e.g., silhouettes-based shape features, histogram of oriented gradient, etc.) are seriously corrupted, and consequently the regressor (trained on un-occluded images) is unable to estimate pose states correctly. In this paper, we present a method that is capable of handling occlusions using sparse signal representations, in which each test sample is represented as a compact linear combination of training samples. The sparsest solution can then be efficiently obtained by solving a convex optimization problem with certain norms (such as l1-norm). The corrupted test image can be recovered with a sparse linear combination of un-occluded training images which can then be used for estimating human pose correctly (as if no occlusions exist). We also show that the proposed approach implicitly performs relevant feature selection with un-occluded test images. Experimental results on synthetic and real data sets bear out our theory that with sparse representation 3D human pose can be robustly estimated when humans are partially or heavily occluded in the scenes.
Estimating Human Pose from Occluded Images (ACCV 2009)Jia-Bin Huang
We address the problem of recovering 3D human pose from single 2D images, in which the pose estimation problem is formulated as a direct nonlinear regression from image observation to 3D joint positions. One key issue that has not been addressed in the literature is how to estimate 3D pose when humans in the scenes are partially or heavily occluded. When occlusions occur, features extracted from image observations (e.g., silhouettes-based shape features, histogram of oriented gradient, etc.) are seriously corrupted, and consequently the regressor (trained on un-occluded images) is unable to estimate pose states correctly. In this paper, we present a method that is capable of handling occlusions using sparse signal representations, in which each test sample is represented as a compact linear combination of training samples. The sparsest solution can then be efficiently obtained by solving a convex optimization problem with certain norms (such as l1-norm). The corrupted test image can be recovered with a sparse linear combination of un-occluded training images which can then be used for estimating human pose correctly (as if no occlusions exist). We also show that the proposed approach implicitly performs relevant feature selection with un-occluded test images. Experimental results on synthetic and real data sets bear out our theory that with sparse representation 3D human pose can be robustly estimated when humans are partially or heavily occluded in the scenes.
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
8. This talk
• Share several useful guidelines for typesetting your paper with LaTeX
• Master the tool so you can maximize the clarity of your paper
• Crowdsource more tricks and best practices
9. Why LaTeX?
• Great typesetting tool (MS Word is terrible at this)
• Style and content separation
• Easier to re-submit the rejected paper to somewhere else (?)
• No need to worry about the numbers of sections, figures, tables
• Beautiful math equations
• Reference management
10. Use the Correct Style File (.sty)
Which one do you want?
• Manually format the paper, e.g.,
All text must be in a two-column format. The total allowable width of the text area is 6 7/8 inches
(17.5 cm) wide by 8 7 8 inches (22.54 cm) high. Columns are to be 3 1/4 inches (8.25 cm) wide,
with a 5/16 inch (0.8 cm) space between them. The main title (on the first page) should begin 1.0
inch (2.54 cm) from the top edge of the page. The second and following pages should begin 1.0
inch (2.54 cm) from the top edge. On all pages, the bottom margin should be 1-1/8 inches (2.86
cm) from the bottom edge of the page for 8.5 × 11-inch paper; for A4 paper, approximately 1-5/8
inches (4.13 cm) from the bottom edge of the page. All printed material, including text,
illustrations, and charts, must be kept within a print area 6-7/8 inches (17.5 cm) wide by 8-7/8
inches (22.54 cm) high.
• Or, just make sure that you use the correct style file
Recommended by Tiffany Yu-Han Chen
11. Version Control
• Version control platform
• Git
• SVN
• Online collaborative editors
• Overleaf
• ShareLaTex
• Pros:
- What-You-See-Is-What-You-Get platform
- Real-time collaborative writing
• Cons: version control is not free
13. Macros – Packages, Latin, and Math
• Commonly used packages
• Figures, algorithms, tables, list, math, fonts, comments, hyperlinks
• See an example here
• Latin abbreviations
• defetal{et~al._} % and others, and co-workers
• defeg{e.g.,~} % for example
• defie{i.e.,~} % that is, in other words
• defetc{etc} % and other things, and so forth
• defcf{cf.~} % compare
• defviz{viz.~} % namely, precisely
• defvs{vs.~} % against
• Math related
• DeclareMathOperator*{argmin}{arg!min}
• DeclareMathOperator*{argmax}{arg!max}
15. Macros – Short-hand notations
Define commonly used notations
• newcommand{tb}[1]{textbf{#1}}
• newcommand{mb}[1]{mathbf{#1}}
• newcommand{Paragraph}[1]{noindenttextbf{#1}}
• defith{i^textit{th}}
Let $mathbf{p}_x^k$,
$mathbf{p}_y^k$,
$mathbf{p}_z^k$ be the …
begin{equation}
mathbf{p}_z^k= mathbf{p}_x^k
+ mathbf{p}_y^k
end{equation}
defpx{mathbf{p}_x^k}
defpx{mathbf{p}_y^k}
defpz{mathbf{p}_z^k}
…
Let $ px, py, pz$ be the …
begin{equation}
pz = px + py
end{equation}
DO NOT type the same symbol more than twice
-> Poor readability, error-prone, difficult to revise
17. Macros – Quickly remove comments
Three easy steps for removing all in-text comments
• Step 1: Include required package usepackage{ifthen}
• Step 2: Put newcommand{final}{1} right below
documentclass
• Step 3: Renew commands if the draft is final
ifthenelse{equal{final}{1}}
{
renewcommand{todo}[1]{}
renewcommand{jiabin}[1]{}
}
{}
Source: Li-Yi Wei and Chia-Kai Liang
20. Subsubsections
subsubsection{XXX}
• 4.1.3 Datatset A
• 4.2.5 Datatset B
• 4.3.1 Metrics
• 4.3.4 Run-time
• 4.5.2 Results on dataset A
• 4.5.3 Results on dataset B
• DO NOT use subsubsections
• Too confusing
• DO use paragraph
subsection{Datasets}
paragraph{Datatset A}
paragraph{Datatset B}
paragraph{Metrics}
subsection{Implementation details}
paragraph{Run-time}
subsection{Results}
paragraph{Results on dataset A}
paragraph{Results on dataset B}
21. Organize your files
• Move figures to separate folders
• Use one tex file for each figure, table, and algorithm
• Leave the main.tex with only main texts
• Help focus on finetuning each figure
• Avoid copying and pasting an entire block of tables/figures
• Use input{FILE_NAME} to include the file to the main paper
• input{figures/teaser}
• input{figures/overview}
• (Optional) Use one tex file for each major section
• Avoid merge/commit conflicts
22. Figures – Teaser
• Show off the strongest results (Input and Output)
[Isola et al 2017]
[Darabi et al. 2012]
[Huang et al 2016]
[Zhang et al 2016]
23. Figures – Motivation
• Examples that highlight the Key Idea of the paper
[Parikh and Grauman 2011]
[Huang et al. 2015]
[Torralba and Efros 2011]
24. Figures – Overview
• Visualize the algorithm
• Provide forward references to
equations and sections
[Girshick 2015][Xue et al. 2015]
[Wadhwa et al. 2013]
[Huang et al. 2016]
25. Figures
• File format
• DO NOT use JPEG images (to avoid compression artifacts). Use PNG or PDF
• Resolution
• DO NOT use low-resolution images
• Position
• Put the figures to the top of each page begin{figure}[t]
• Caption
• The image caption should be self-contained
• Highlight the topic of the figure with bold font
textbf
[Faktor and Irani 2014]
26. Multiple Images
• Use subfigure or minipage. DO NOT use tabular.
• Never manually define the physical size of the image
• includegraphics[width=5cm]{IMAGE.png} -> Bad
• includegraphics[width=0.5linewidth]{IMAGE.png} -> Good
• setlength{figwidth}{0.5linewidth} -> Best
begin{minipage}{figwidth}
includegraphics[width=linewidth]{IMAGE.png}
end{minipage}
27. Multiple Images
• Put sub-captions directly under subfigures, do not put them in the
caption
• All the legends, axis, labels must be clearly visible
• Make use of color and textures to code information
(a) (b) PatchMatch propagation Flow-guided propagation
[Huang et al. 2016]
30. Image, video, and dataset names
• Use textsc{Name} to separate images, videos, dataset names
from the main texts.
[Kopf 2016]
31. Multiple Images
• How do I align images with different sizes?
• Solve a simple algebra problem
• Suppose we know the image on the left has aspect ratio = H/W = c
• What’s 𝑥 ?
ImA
ImB
ImB
𝑥
𝑐𝑥
1 − 𝑥
1 − 𝑥
1 − 𝑥
𝑐𝑥 = 2 1 − 𝑥
2 + 𝑐 𝑥 = 2
𝑥 = 2/(2 + c)
setlength{figa}{0.612textwidth}
setlength{figb}{0.388textwidth}
begin{minipage}{figa}
includegraphics[width=linewidth]{ImA.png}
end{minipage}
begin{minipage}{figb}
includegraphics[width=linewidth]{ImB.png}
includegraphics[width=linewidth]{ImB.png}
end{minipage}
32. Tables – Basics
begin{table}[t]
caption{Table caption} % Table captions are ABOVE the table
label{tab:table_name} % Always label the table
begin{tabular}{clr} % c: center, l: left, r: right
XX & XX & XX
YY & YY & YY
end{tabular}
end{table}
User-friendly LaTeX table generator (recommended by Ting-Hao Kenneth Huang)
33. Tables – Comparison to related work
• Provide conceptual differences to
related work
[Zhang et al 2017]
[Lai et al 2016]
34. Tables – Results
• Highlight the best and the second best results
• Group methods that use different training sets or different levels of
supervision
• Always provide citation for each method
• If you have a big table, use
resizebox{textwidth}{!}{
begin{tabular}
…
end{tabular}
}
35. Tables – Making nice tables
• Which one looks better?
Source: Small Guide to Making Nice Tables by Markus Püschel (ETH Zürich)
Recommended by David J. Crandall
36. Algorithms
• See the documentation of algorithm2e
• Provide the main steps of the algorithm
• Use consistent annotations
• Use references to sections and
equations to connect the main texts
with the algorithm
[Huang et al. 2016]
37. Equations
• Use begin{equation}…end{equation} environment.
• Use begin{algin} … end{align} if you have multiple lines of
equations
• Label every equation label{eqn:Eqn-Name}
• For in-text math symbols, use $$, e.g. Let $x$ be …
• Define every notation
• For texts that are not part of the equation, use mathrm, e.g.
$x_mathrm{color}$
38. Equations
• Number all equations
• Easy to refer to them
• Equations are grammatical parts of the sentences
• Never forget a period after an equation
• Never create a dangling displayed equation
• Negative numbers
• “-” indicate the dash. Use $-1$ to represent minus one
• Angle braskets
• Use langle and rangle, instead of the comparison operators < and >
• Big parentheses
• Use left and right for automatic resizing round (), square [], and angled
langlerangle brackets as well as vertical bars vert and Vert
Source: https://www.cs.dartmouth.edu/~wjarosz/writing.html
39. Dashes
• hyphen (-, produced with one dash -)
• interword dashes
• E.g., non-negligible
• en-dash (–, produced with two dashes --)
• indicate an opposition or relationship
• e.g., mass--energy equivalence → “mass–energy equivalence”
• Pages
• e.g., as seen on pages 17--30 → “as seen in on pages 17–30”
• em-dash (—, produced with three dashes ---)
• denote a break in a sentence or to set off parenthetical statements
• e.g., A flock of sparrows – some of them juveniles – flew overhead
Source: https://www.cs.dartmouth.edu/~wjarosz/writing.html
40. References
• Paper title:
• Use correct capital letter, e.g., ImageNet -> Image{N}et
• The first letter after ``:'' should be capital, e.g., DeepPose: Human pose
estimation ... -> Deep{P}ose: {H}uman pose estimation ...
• Authors:
• Make sure that you use ``{}'' for special letters, e.g., Durand, Fr{'e}do.
• Journal papers
• Fill in authors, title, journal, volume, number, pages, year.
Conference papers
• Only fill in authors, title, booktitle, and year.
• Do not fill in volume, number, page, and publisher.
41. References
• Journal/conference venue:
• Use the pre-defined string
@string { ICCV = "International Conference on Computer Vision" }
booktitle = ICCV
• Be consistent
• Do not use ``IEEE Transcations on Pattern Analysis and Machine Intelligence'',
``Pattern Analysis and Machine Intelligence, IEEE Trasactions on'', ``IEEE Trans.
PAMI'', ``TPAMI'' at the same time. Using the pre-defined strings can help
avoid this issue.
• Label:
• Recommended naming convention: Last name of the first author-Publication-
Year, e.g., Huang-CVPR-2015.
42. References
• Avoid multiple entries of the same paper
• Find the correct venue where the paper was published
• Do not use arXiv for every paper
• Manage the references
• Group the papers into different categories
43. Citations
• Do not use citations as nouns
• If you remove all parenthetical citations from the paper, you should still have
complete, grammatically correct sentences
• “As shown in [1]” -> “As shown by XXX et al. [1]”
• No “[1] present XXX…”
• Spacing
• Use a non-breaking space “~” between a citation and the preceding word in
the sentence: “Path tracing~cite{Kajiya:86} is...”.
• Multiple citations
• Use cite{key1,key2}
• Do not use cite{key1}cite{key2}
Source: https://www.cs.dartmouth.edu/~wjarosz/writing.html
44. Fit your paper into the page limit
Step 1. Use consistent lengths for reducing margins
newlengthsecmargin
newlengthparamargin
newlengthfigmargin
setlength{secmargin}{-1.0mm}
setlength{paramargin}{-2.0mm}
setlength{figmargin}{-3.0mm}
Step 2. Apply the vspace to the corresponding positions
vspace{secmargin} vspace{paramargin} vspace{figmargin}
Step 3. Adjust baseline
renewcommand{baselinestretch}{0.998}
45. Better tool than LaTeX?
• https://www.authorea.com/
Recommended by Tzu-Mao Li
46. Resources on Writing
• Awesome computer vision – writing by Jia-Bin Huang (Virginia Tech)
• A quick guide to LaTeX by Dave Richeson (Dickinson College)
• Common mistakes in technical writing by Wojciech Jarosz (Dartmouth
College)
• SIGGRAPH paper template by Li-Yi Wei (University of Hong Kong)
• Notes on writing by Fredo Durand (MIT)
• How to write a good CVPR submission by Bill Freeman (MIT)
• How to write a great research paper by Simon Peyton Jones (MSR)
• How to write papers so people can read them by Derek Dreyer (MPI)
47. Thank You!
• Please let me know (jbhuang@vt.edu) if you would like to share your
best practices