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A Github Pages template for academic websites. This was forked (then detached) by Stuart Geiger from the Minimal Mistakes Jekyll Theme, which is © 2016 Michael Rose and released under the MIT License. See LICENSE.md.

I think I've got things running smoothly and fixed some major bugs, but feel free to file issues or make pull requests if you want to improve the generic template / theme.

Note: if you are using this repo and now get a notification about a security vulnerability, delete the Gemfile.lock file.

Instructions

  1. Register a GitHub account if you don't have one and confirm your e-mail (required!)
  2. Fork this repository by clicking the "fork" button in the top right.
  3. Go to the repository's settings (rightmost item in the tabs that start with "Code", should be below "Unwatch"). Rename the repository "[your GitHub username].github.io", which will also be your website's URL.
  4. Set site-wide configuration and create content & metadata (see below -- also see this set of diffs showing what files were changed to set up an example site for a user with the username "getorg-testacct")
  5. Upload any files (like PDFs, .zip files, etc.) to the files/ directory. They will appear at https://[your GitHub username].github.io/files/example.pdf.
  6. Check status by going to the repository settings, in the "GitHub pages" section
  7. (Optional) Use the Jupyter notebooks or python scripts in the markdown_generator folder to generate markdown files for publications and talks from a TSV file.

See more info at https://academicpages.github.io/

To run locally (not on GitHub Pages, to serve on your own computer)

  1. Clone the repository and made updates as detailed above
  2. Make sure you have ruby-dev, bundler, and nodejs installed: sudo apt install ruby-dev ruby-bundler nodejs
  3. Run bundle clean to clean up the directory (no need to run --force)
  4. Run bundle install to install ruby dependencies. If you get errors, delete Gemfile.lock and try again.
  5. Run bundle exec jekyll liveserve to generate the HTML and serve it from localhost:4000 the local server will automatically rebuild and refresh the pages on change.

Changelog -- bugfixes and enhancements

There is one logistical issue with a ready-to-fork template theme like academic pages that makes it a little tricky to get bug fixes and updates to the core theme. If you fork this repository, customize it, then pull again, you'll probably get merge conflicts. If you want to save your various .yml configuration files and markdown files, you can delete the repository and fork it again. Or you can manually patch.

To support this, all changes to the underlying code appear as a closed issue with the tag 'code change' -- get the list here. Each issue thread includes a comment linking to the single commit or a diff across multiple commits, so those with forked repositories can easily identify what they need to patch.

Yasar Abbas's Projects

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Segment-CNN: A Framework for Temporal Action Localization in Untrimmed Videos via Multi-stage CNNs

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Stereo Face Liveness Detection using Convolutional Neural Networks

soundr-dataset icon soundr-dataset

Dataset of "Soundr: Head Position and Orientation Prediction Using a Microphone Array"

sum-gan-aae icon sum-gan-aae

A PyTorch Implementation of SUM-GAN-AAE from "Unsupervised Video Summarization via Attention-Driven Adversarial Learning" (MMM 2020)

sum-gan-sl icon sum-gan-sl

A PyTorch Implementation of SUM-GAN-sl from "A Stepwise, Label-based Approach for Improving the Adversarial Training in Unsupervised Video Summarization" (AI4TV Workshop at ACM MM 2019)

swav icon swav

PyTorch implementation of SwAV https//arxiv.org/abs/2006.09882

transnet icon transnet

TransNet: A deep network for fast detection of common shot transitions

turn-tap icon turn-tap

TURN TAP: Temporal Unit Regression Network for Temporal Action Proposals

ubicoustics icon ubicoustics

Accompanying repository for Ubicoustics: Plug-and-Play Acoustic Activity Recognition

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supplementary material for paper "VCGAN: Video Colorization with Hybrid Generative Adversarial Network", IEEE Transactions on Multimedia (TMM'21)

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code for our ECCV-2020 paper: Self-supervised Video Representation Learning by Pace Prediction

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A PyTorch reimplementation of FCSN in paper "Video Summarization Using Fully Convolutional Sequence Networks"

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VISSL is FAIR's library of extensible, modular and scalable components for SOTA Self-Supervised Learning with images.

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