Motion

Motion and Shape Capture from Sparse Markers

This repository contains the official chumpy implementation of mocap body solver used for AMASS: AMASS: Archive of Motion Capture as Surface ShapesNaureen Mahmood, Nima Ghorbani, Nikolaus F. Troje, Gerard Pons-Moll, Michael J. BlackFull paper |Video |Project website |Poster Description This repository holds the code for MoSh++, introduced in AMASS, ICCV’19.MoSh++ is the upgraded version of MoSh, Sig.Asia’2014.Given a labeled marker-based motion capture (mocap) c3d file and the correspondencesof the marker labels to the locations on the body, MoSh canreturn model […]

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Bot

The Few-Shot Bot: Prompt-Based Learning for Dialogue Systems

This repository includes the dataset, experiments results, and code for the paper: Few-Shot Bot: Prompt-Based Learning for Dialogue Systems PDF. Authors: Andrea Madotto, Zhaojiang Lin, Genta Indra Winata, Pascale Fung Abstract Learning to converse using only a few examples is a grand challenge in Conversational AI. The current best conversational models, which are either good chit-chatters (e.g., BlenderBot) or goal-oriented systems (e.g., MinTL), are language models (LMs) fine-tuned on large conversational datasets. Training these models is expensive, both in terms […]

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Detection

Morphable Detector for Object Detection on Demand

(ICCV 2021) PyTorch implementation of the paper Morphable Detector for Object Detection on Demand. If our project is helpful for your research, please consider citing: @inproceedings{zhaomorph, author = {Xiangyun Zhao, Xu Zou, Ying Wu}, title = {Morphable Detector for Object Detection on Demand}, booktitle = {ICCV}, Year = {2021} } Install First, install PyTorch and torchvision. We have tested on version of 1.8.0 with CUDA 11.0, but the other versions should also be working. Our code is    

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Transformer

Revitalizing CNN Attention via Transformers in Self-Supervised Visual Representation Learning

This repository is the official implementation of CARE paper. Updates (09/10/2021) Our paper is accepted by NeurIPS 2021. (14/10/2021) Our code is available for ssl pretraining and image classification evaluation. Comming Provide resnet_50(100e) models and logs. Complete the pretrained models and logs. Requirements To install requirements: conda create -n care python=3.6 conda install pytorch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2 cudatoolkit=10.1    

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Docker

A simple docker-compose app for orchestrating a fastapi application, a celery queue with rabbitmq(broker) and redis(backend)

A simple docker-compose app for orchestrating a fastapi application, a celery queue with rabbitmq(broker) and redis(backend) build and run containers docker-compose up -d –build This will expose fastapi application on 5000 and celery flower on 5555 swagger docs – http://localhost:5000/ redoc – http://localhost:5000/redoc celery flower – http://localhost:5555 GitHub https://github.com/karthikasasanka/fastapi-celery-redis-rabbitmq    

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FastAPI

Backend Skeleton using FastAPI and Sqlalchemy ORM

Based on @tiangolo’s full stack postgres template, with some things added, some things removed, and some things changed. This is a work in progress! TODO: Remove fastapi_util from the app repo, and make it a separate package Add example pydantic models, sqlalchemy models, endpoints Add encode/databases support (possibly in a separate template?) GitHub https://github.com/dmontagu/backend-skeleton    

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Print

Prints values and types during compilation

Compile-Time Printer Compile-Time Printer prints values and types at compile-time in C++. Teaser test.cpp compile-time-printer — make test.cpp #include template constexpr auto func(int i) { // Formatted output. ctp::printf(“Hello {}!n”, ctp::type{I}); // Standardized output. ctp::print(I + i, sizeof(I)); return true; } constexpr    

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Detection

Structure Guided Lane Detection with python

Project for the IJCAI 2021 paper “Structure Guided Lane Detection” Abstract Recently, lane detection has made great progresswith the rapid development of deep neural networksand autonomous driving. However, thereexist three mainly problems including characterizinglanes, modeling the structural relationship betweenscenes and lanes, and supporting more attributes(e.g., instance and type) of lanes. In thispaper, we propose a novel structure guided frameworkto solve these problems simultaneously. Inthe framework, we first introduce a new lane representationto characterize each instance. Then a topdownvanishing point guided […]

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