Task

Exploring the Dual-task Correlation for Pose Guided Person Image Generation

The source code for our paper “Exploring Dual-task Correlation for Pose Guided Person Image Generation“, Pengze Zhang, Lingxiao Yang, Jianhuang Lai, and Xiaohua Xie, CVPR 2022. Abstract Pose Guided Person Image Generation (PGPIG) is the task of transforming a person image from the source pose to a given target pose. Most of the existing methods only focus on the ill-posed source-to-target task and fail to capture reasonable texture mapping. To address this problem, we propose a novel Dual-task Pose Transformer […]

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Converter

Mercury: easily convert Python notebook to web app and share with others

Mercury is a perfect tool to convert Python notebook to web app and share with non-programmers. You define interactive widgets for your notebook with the YAML header. Your users can change the widgets values, execute the notebook and save result (as html file). You can hide your code to not scare your (non-coding) collaborators. Easily deploy to any server. Mercury is dual-licensed. Looking for dedicated support, a commercial-friendly license, and more features? The Mercury Pro is for    

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Machine Learning

Colar: Effective and Efficient Online Action Detection by Consulting Exemplars, CVPR 2022

This repository is the official implementation of Colar. In this work, we study the online action detection and develop an effective and efficient exemplar-consultation mechanism. Paper from arXiv. Requirements To install requirements: conda env create -n env_name -f environment.yaml Before running the code, please activate this conda environment. Data Preparation a. Download pre-extracted features from baiduyun (code:cola) Please ensure the data structure is as below ├── data └── thumos14    

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ddos

A DDoS ATTACK SCRIPT WITH PYTHON

#Cybersecurity #Cybersec #Securitytalk #DDOSAttack #Breakinternet DDoS attacks occur when a cyber-criminal floods a targeted organization’s network with access requests; this initially disrupts service by denying legitimate requests from actual customers, and eventually overloads the network until it crashes. Criminals execute their DDoS attacks by sending out malicious code to hundreds or even thousands of computers, instructing each one to send requests to a single organization. This is usually accomplished through a botnet; a network of private computers infected with malicious […]

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Function

Python humanize functions

This modest package contains various common humanization utilities, like turninga number into a fuzzy human-readable duration (“3 minutes ago”) or into ahuman-readable size or throughput. It is localized to: Bengali Brazilian Portuguese Catalan Danish Dutch European Portuguese Finnish French German Indonesian Italian Japanese Korean Persian Polish Russian Simplified Chinese Slovak Slovenian Spanish Swedish Turkish Ukrainian Vietnamese API reference https://python-humanize.readthedocs.io Usage Integer humanization

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Machine Learning

Dynamic MLP for Fine-Grained Image Classification by Leveraging Geographical and Temporal Information

Dynamic MLP, which is parameterized by the learned embeddings of variable locations and dates to help fine-grained image classification. Requirements Experiment Environment python 3.6 pytorch 1.7.1+cu101 torchvision 0.8.2 Get pretrained models for SK-Res2Net following here.Get datasets following here. Train the model 1. Train image-only model Specify –image_only for training image-only models. ResNet-50 (67.924% Top-1 acc) CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python3 train.py –name res50_image_only –

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Machine Learning

BatchFormer: Learning to Explore Sample Relationships for Robust Representation Learning

All Codes in the paper will be coming soon. Here is the Pytorch Code of BatchFormer def BatchFormer(x, y, encoder, is_training): # x: input features with the shape [N, C] # encoder: TransformerEncoderLayer(C,4,C,0.5) if not is_training: return x, y pre_x = x x = encoder(x.unsqueeze(1)).squeeze(1) x = torch.cat([pre_x, x], dim=0) y = torch.cat([y, y], dim=0) return x, y If you find this repository helpful, please consider cite:

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State

Dialogue Summaries as Dialogue States (DS2)

Installing the directory as pip will resolve all path issues pip install -e . pip install -r requirements.txt # requires python 3.8 Get MWOZ data (for 2.0 change all 2.1 to 2.0) For 2.0 python scripts/create_data_mwoz.py –mwz_ver=2.0 –main_dir=data_mwoz_2.0 –target_path=data_mwoz_2.0/mwz For 2.1 python scripts/create_data_mwoz.py –mwz_ver=2.1 –main_dir=data_mwoz_2.1 –target_path=data_mwoz_2.1/mwz Training and Inference – Cross-domainPre-trainingExample using T5 on Cross-domain pre-training. Note that this code will not work yet because we did not release our pretrained model checkpoints yet due to anonymity issues. We will […]

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Tool

Proxmox based VDI client

This project’s focus is to create a simple VDI client intended for mass deployment. This VDI client connects directly to Proxmox VE and allows users to connect (via Spice) to any VMs they have permission to access. Windows Installation You MUST install virt-viewer prior to using PVE VDI client, you may download it from the official Virtual Machine Manager site. Please visit the releases section to download a prebuilt MSI package If you need to customize the installation, such as […]

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