This is the official implementation of SeqTR: A Simple yet Universal Network for Visual Grounding, which simplifies and unifies the modelling for visual grounding tasks under a novel point prediction paradigm. Installation Prerequisites pip install -r requirements.txt wget https://github.com/explosion/spacy-models/releases/download/en_vectors_web_lg-2.1.0/en_vectors_web_lg-2.1.0.tar.gz -O en_vectors_web_lg-2.1.0.tar.gz pip install en_vectors_web_lg-2.1.0.tar.gz Then install SeqTR package in editable mode: Data Preparation Download our preprocessed json files including the merged dataset for pre-training, and DarkNet-53 model weights trained on MS-COCO object detection task. Download the train2014 images from mscoco […]

This computational music theory project assigns a brightness score for all 28 modes derived from four jazz scales: major, melodic minor, harmonic minor, and harmonic major. I constructed scale networks to visualize the interrelations between the 28 modes. From these networks I found 18 “rules” for modulation that allow for maximally smooth voice leading. Other applications and experiments found in this code repository include characterizing and ranking the brightness of 1) all triad combos, 2) all 7th chords added to […]

@inproceedings{ZoomNet-CVPR2022, title = {Zoom In and Out: A Mixed-scale Triplet Network for Camouflaged Object Detection}, author = {Pang, Youwei and Zhao, Xiaoqi and Xiang, Tian-Zhu and Zhang, Lihe and Lu, Huchuan}, booktitle = CVPR, year = {2022} } Changelog 2022-03-05: Update weights and results links. Fixed some bugs. Update dataset links. Update    

ThorFI: a Novel Approach for Network Fault Injection as a Service This repo includes ThorFI, a novel fault injection solution for virtual networks in cloud computing infrastructures. ThorFI is designed to provide non-intrusive fault injection capabilities for a cloud tenant, and to isolate injections from interfering with other tenants on the infrastructure. Currently, ThorFI supports OpenStack cloud management platform.ThorFI details are reported into the paper “ThorFI: a Novel Approach for Network Fault Injection as a Service” accepted for publication in […]

Welcome to the world of Networking. An ongoing curated collection of awesome software, libraries, frameworks, talks & videos, best practices, learning tutorials and important practical resources about networking in cybersecurity. Thanks to all contributors, you’re awesome and wouldn’t be possible without you! Our goal is to build a categorized community-driven collection of very well-known resources. Table of Contents Network Foundations Computer networking refers to connected computing devices (such as laptops, desktops, servers, smartphones, and tablets) and an ever-expanding array of […]

NuPIC Studio NuPIC Studio is an all­-in-­one tool that allows users create a HTM neural network from scratch, train it, collect statistics, and share it among the members of the community. It is not just a visualization tool but an HTM builder, debugger and laboratory for experiments. It is ideal for newbies with little intimacy with NuPIC code as well as experts that wish a better productivity. Among its features and advantages: Users can open, save, or change their “HTM […]

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.

This project is the offical PyTorch implementation of HDN(Homography Decomposition Networks) for planar object tracking.(AAAI 2022, Accepted) Project Page | Paper @misc{zhan2021homography, title={Homography Decomposition Networks for Planar Object Tracking}, author={Xinrui Zhan and Yueran Liu and Jianke Zhu and Yang Li}, year={2021}, eprint={2112.07909}, archivePrefix={arXiv}, primaryClass={cs.CV} } Installation Please find installation instructions in INSTALL.md. Quick Start: Using HDN Add HDN to your PYTHONPATH

MUC Next Check-ins Prediction via History and Friendship on Location-Based Social Networks (MDM 2018) Performance Details for Accuracy: | Dataset | [email protected] | [email protected] | [email protected] | | ———- | ————| ————-| —————| | Foursquare | 0.8389 | 0.9105 | 0.9368 | | Gowalla | 0.7522 | 0.846 | 0.8866 | The performance of our framework on Foursquare and Gowalla.   To finish reading, please visit source site