January 9, 2022 Self-Supervised

A self-supervised learning framework for audio-visual speech

Learning Audio-Visual Speech Representation by Masked Multimodal Cluster Prediction Robust Self-Supervised Audio-Visual Speech Recognition Introduction AV-HuBERT is a self-supervised representation learning framework for audio-visual speech. It achieves state-of-the-art results in lip reading, ASR and audio-visual speech recognition on the LRS3 audio-visual speech benchmark. If you find AV-HuBERT useful in your research, please use the following BibTeX entry for citation.

January 9, 2022 Neural Network

A simple Neural Network from scratch in Python using the Pymathrix library

A Simple Neural Network from scratch in Python using the Pymathrix library.Check the Pymathrix documentation. Usage Import the Pymathrix library into your python code: >> inputs = px.matrix(1, 3) >>> inputs.assign([1, 1, -1])” readability=”10″> >>> inputs = px.matrix(1, 3) >>> inputs.assign([1, 1, -1]) Create the neural network object:

January 9, 2022 Slash

Your custom slash commands Discord bot

Hey, I’m Slashy – your friendly neighborhood custom-command bot! The code for this bot exists because I’m like the open-source community, so feel free to run it yourself, or submit pull requests to improve it. You can get help on using the bot with the /help command in any of the servers Slashy is in, or in Slashy’s DM-s. Invite Click right here to add Slashy to your own server, or just copy https://hey.imkez.com/slashy-invite License This project is licensed under […]

January 9, 2022 GUI

A simple GUI complex backend Chat Application made using python

Python Chat Application ( Please Star this if you like this or have used this . It’s free 🙂 ) Table of Contents ChatterPatter is a simple GUI based chat application which includes both SERVER AND CLIENT scirpts in same application. Simple in front , complex in back . So complex that even the developer fears going through it again and again.Developed solely on Python. Python Libraries and Modules Used Sockets Threading Tkinter (Tabs , Listbox , Labels , Entry […]

January 9, 2022 Keyboard

Measure edit distance based on keyboard layout

Measure edit distance based on keyboard layout. Introduction Default edit distances, such as the Levenshtein distance, don’t differentiate between characters. The distance between two characters is either 0 or 1. This package allows you to measure edit distances by taking into account keyboard layouts. The scope is purposefully limited to alphabetical, numeric, and punctuation keys. That’s because this package is meant to assist in analyzing user inputs — e.g. for spelling correction in a search engine. The goal of this […]

January 9, 2022 Neural Machine Translation (NMT), NMT Leave a comment

Machine Translation Weekly 98: XGLM: GPT-3 for 30 languages

By the end of the year, Meta AI (previously Facebook AI) published a pre-print introducing a multilingual version of GPT-3 called XGLM. As its title – Few-shot Learning with Multilingual Language Models – suggests, it explores the few-shot learning capabilities. The main takeaways are: Good news: It is indeed possible to train such a model and it works somehow. Bad news 1: Cross-lingual transfer of few-shot learned tasks is not as good as I would expect. Bad news 2: Huge […]

January 8, 2022 Network

Band-limited Coordinate Networks for Multiscale Scene Representation

Project Page | Video | Paper Official PyTorch implementation of BACON.BACON: Band-limited Coordinate Networks for Multiscale Scene RepresentationDavid B. Lindell*,Dave Van Veen,Jeong Joon Park,Gordon WetzsteinStanford University Quickstart To setup a conda environment use these commands conda env create -f environment.yml conda activate bacon # download all datasets python download_datasets.py Now you can train networks to fit a 1D function, images, signed distance fields, or neural radiance fields with the following commands.

January 8, 2022 Real Time

FADNet++: Real-Time and Accurate Disparity Estimation with Configurable Networks

This repository contains the code (in PyTorch) for the “FADNet++” paper. Contents Introduction Usage Results Acknowledgement Contacts Introduction We propose an efficient and accurate deep network for disparity estimation named FADNet with three main features: It exploits efficient 2D based correlation layers with stacked blocks to preserve fast computation. It combines the residual structures to make the deeper model easier to learn. It contains multi-scale predictions so as to exploit a multi-scale weight scheduling training technique to improve the accuracy. […]

January 8, 2022 Video

Deepstream python play rtsp h264

deepstream python play rtsp h264 n gstreamer python play rtsp h264 and h265 n common=> deepstream_python_apps=>apps n dstest3_pgie_config.txt ： test pgie n deepstream_rtsp_h264.py ：play one rtsp h264 n deepstream_rtsps_h264.py ：play Multiple rtsp h264 n deepstream_videos_h264.py ：play Multiple video h264 n gstreamer_test_h264.py ：play one rtsp h264 n gstreamer_test_h265.py ：play one rtsp h265 n GitHub View Github

January 8, 2022 COVID-19

Backend code for covid-polygraph

Covid-polygraph, a set of Machine Learning-driven fact-checking tools that aim to address the issue of misleading information related to COVID-19. Project is extended based on our CS3244 Team Projectand more on code reference and reuse can be found in the Offline Training Pipeline Repository. How to run the backend code To run the code, use Docker: docker build . -t covid-polygraph:latest Afterwards, you can start the backend locally using the docker image built. For our team, we deploy this image […]

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