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ailab

Experience, Learn and Code the latest breakthrough innovations with Microsoft AI

bootstrap.pytorch

Highlevel framework for starting Deep Learning projects (lightweight, flexible, easy to extend)

bottom-up-attention

Bottom-up attention model for image captioning and VQA, based on Faster R-CNN and Visual Genome

bottom-up-attention-vqa

An efficient PyTorch implementation of the winning entry of the 2017 VQA Challenge.

chineseocr_lite

超轻量级中文ocr，支持竖排文字识别, 支持ncnn推理 ( dbnet(1.8M) + crnn(2.5M) + anglenet(378KB)) 总模型仅4.7M

clash_for_windows_pkg

A Windows/macOS GUI based on Clash

cognitive-services-quickstart-code

Code Examples used by the Quickstarts in the Cognitive Services Documentation

cognitive-services-rest-api-samples

This is a repo for cognitive services REST API samples in 4 languages: C#, Java, Node.js, and Python.

cs-notes

:books: 技术面试必备基础知识、Leetcode、计算机操作系统、计算机网络、系统设计、Java、Python、C++

epidemicmanagement

This is a project finished by me and my teammates to help people prevent and control the coronavirus.

faster-rcnn.pytorch

A faster pytorch implementation of faster r-cnn

gvqa

Code for the Grounded Visual Question Answering (GVQA) model from the paper -- Don't Just Assume; Look and Answer: Overcoming Priors for Visual Question Answering

hiecoattenvqa

homework

imageqa-san

code for Stacked attention networks for image question answering

javaguide

「Java学习+面试指南」一份涵盖大部分 Java 程序员所需要掌握的核心知识。准备 Java 面试，首选 JavaGuide！

jeecg-boot

基于代码生成器的低代码平台，超越传统商业平台！前后端分离架构SpringBoot 2.x，SpringCloud，Ant Design&Vue，Mybatis-plus，Shiro，JWT。强大的代码生成器让前后端代码一键生成，无需写任何代码! 引领新低代码开发模式OnlineCoding->代码生成->手工MERGE，帮助Java项目解决70%重复工作，让开发更关注业务，既能快速提高开发效率，帮助公司节省成本，同时又不失灵活性。

murel.bootstrap.pytorch

MUREL (CVPR 2019), a multimodal relational reasoning module for VQA

myfirstc

mygcp

proxy_pool

Python爬虫代理IP池(proxy pool)

pytorch-vqa

Strong baseline for visual question answering

qqai

简单易用的腾讯AI接口

recognizers-text

Microsoft.Recognizers.Text provides recognition and resolution of numbers, units, and date/time expressed in multiple languages (ZH, EN, FR, ES, PT, DE, IT, TR, HI. Partial support for NL, JA, KO, SV). Contributions are greatly welcome! Packages are available at https://www.nuget.org/profiles/Recognizers.Text and https://www.npmjs.com/~recognizers.text

skip-thoughts

Sent2Vec encoder and training code from the paper "Skip-Thought Vectors"

up-down-captioner

Automatic image captioning model based on Caffe, using features from bottom-up attention.

vqa

vqa-mcb

vqa2.0-recent-approachs-2018.pytorch

A pytroch reimplementation of "Bilinear Attention Network", "Intra- and Inter-modality Attention", "Learning Conditioned Graph Structures", "Learning to count object", "Bottom-up top-down" for Visual Question Answering 2.0

vqa_lstm_cnn

Train a deeper LSTM and normalized CNN Visual Question Answering model. This current code can get 58.16 on OpenEnded and 63.09 on Multiple-Choice on test-standard.