Month: July 2021

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A Simple Strong Baseline for TextVQA and TextCaps

Simple is not Easy Simple is not Easy: A Simple Strong Baseline for TextVQA and TextCaps[AAAI2021] Citation If you use ssbaseline in your work, please cite: @article{zhu2020simple, title={Simple is not Easy: A Simple Strong Baseline for TextVQA and TextCaps}, author={Zhu, Qi and Gao, Chenyu and Wang, Peng and Wu, Qi}, journal={arXiv preprint arXiv:2012.05153}, year={2020} } Installation First install the repo using git clone https://github.com/ZephyrZhuQi/ssbaseline.git ~/ssbaseline cd ~/ssbaseline python setup.py build develop Getting Data We provide SBD-Trans OCR for TextVQA and […]

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Machine Learning

End-to-End Pre-training for Vision-Language Representation Learning

Seeing Out of tHe bOx End-to-End Pre-training for Vision-Language Representation Learning [CVPR’21, Oral]By Zhicheng Huang*, Zhaoyang Zeng*, Yupan Huang*, Bei Liu, Dongmei Fu and Jianlong Fu arxiv: https://arxiv.org/pdf/2104.03135.pdf This is the official implementation of the paper. In this paper, we propose SOHO to “See Out of tHe bOx” that takes a whole image as input, and learns vision-language representation in an end-to-end manner. SOHO does not require bounding box annotations which enables inference 10 times faster than region-based approaches. Architecture […]

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Images

A public available dataset for road boundary detection in aerial images

Topo-boundary This is the official github repo of paper Topo-boundary: A Benchmark Dataset on Topological Road-boundary Detection Using Aerial Images for Autonomous Driving. Project page. Topo-boundary is a publicly available benchmark dataset for topological road-boundary detection in aerial images. With an aerial image as the input, the evaluated method should predict the topological structure of road boundaries in the form of a graph. This dataset is based on NYC Planimetric Database. Topo-boundary consists of 25,297 4-channel aerial images, and each […]

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Visualization

Forecasting directional movements of stock-prices for intraday trading using LSTM and random-forest

Stock-market-forecasting Forecasting directional movements of stock-prices for intraday trading using LSTM and random-foresthttps://arxiv.org/abs/2004.10178Pushpendu Ghosh, Ariel Neufeld, Jajati K Sahoo We design a highly profitable trading stratergy and employ random forests and LSTM networks (more precisely CuDNNLSTM) to analyze their effectiveness in forecasting out-of-sample directional movements of constituent stocks of the S&P 500, for intraday trading, from January 1993 till December 2018. Bibtex @article{ghosh2021forecasting, title={Forecasting directional movements of stock prices for intraday trading using LSTM and random forests}, author={Ghosh, Pushpendu and […]

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PyTorch

Face Identity Disentanglement via Latent Space Mapping

ID-disentanglement-Pytorch Pytorch implementation of the paper Face Identity Disentanglement via Latent Space Mapping for both training and evaluation, with StyleGAN 2. Changes from original paper instead of using a Discriminator loss for the mapper. We have used several other losses such as: LPIPS Loss (The Unreasonable Effectiveness of Deep Features as a Perceptual Metric, Zhang el al, 2018) MSE Loss Different ID Loss Different landmark detector The reason for those changes resides in the fact that the training procedure with […]

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Wrapper

A python wrapper over OpenGL 3.3+ core that simplifies the creation of simple graphics

ModernGL ModernGL is a python wrapper over OpenGL 3.3+ core that simplifies the creation of simple graphics applications like scientific simulations, games or user interfaces. Usually, acquiring in-depth knowledge of OpenGL requires a steep learning curve. In contrast, ModernGL is easy to learn and use, moreover it is capable of rendering with high performance and quality, with less code written. The majority of the moderngl code base is also written in C++ for high performance. pip install moderngl Features GPU […]

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New Future of Work: Managing IT and security in remote scenarios with Jaime Teevan and Matt Brodsky

Episode 130 | July 29, 2021 For Microsoft researchers, COVID-19 was a call to action. The reimagining of work practices had long been an area of study, but existing and new questions that needed immediate answers surfaced as companies and their employees quickly adjusted to significantly different working conditions. Teams from across the Microsoft organizational chart pooled their unique  

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Markdown

A markdown extension for converting Leiden+ epigraphic text to TEI XML/HTML

$ pip install leidenmark A Python Markdown extension for converting Leiden+ epigraphic text to TEI XML/HTML. Inspired by the Brill plain text (BPT) format that aims to incorporate Leiden+ into a Markdown-based syntax. >>> from leidenmark import leiden_plus >>> content = “”” “”” >>> leiden_plus(content, indent=True) The output of the above lines is the following XML snippet: Lorem ipsum dolor sit amet, conc etur adipiscing ut labore et dol ore magna    

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Testing

Virt Test Provider for qemu and other related virtualization backends

QEMU test provider for virt-test This is the official [1] test provider for the following subtest types: QEMU Generic (Virtualization backend agnostic) OpenVSwitch Really quick start guide Fork this repo on github Create a new topic branch for your work Create a new test provider file in your virt test repo, like: cp io-github-autotest-qemu.ini myprovider.ini [provider]uri: file:///home/foo/Code/tp-qemu[generic]subdir: generic/[qemu]subdir: qemu/[openvswitch]subdir: openvswitch/ You can optionally delete temporarily the io-github-autotest-qemu.ini file, just so you don’t have test conflicts. Then you can develop your […]

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