COIN the currently largest dataset for comprehensive instruction video analysis.

Overview

COIN Dataset

COIN is the currently largest dataset for comprehensive instruction video analysis. It contains 11,827 videos of 180 different tasks (i.e., car polishing, make French fries) related to 12 domains (i.e., vehicle, dish). All videos are collected from YouTube and annotated with an efficient toolbox.

Authors and Contributors

Yansong Tang*, Dajun Ding, Yongming Rao*, Yu Zheng*, Danyang Zhang*, Lili Zhao, Jiwen Lu*, Jie Zhou*, Yongxiang Lian*, Yao Li, Jiali Sun, Chang Liu, Dongge You, Zirun Yang, Jiaojiao Ge, Jiayun Wang*

  • *Tsinghua University
  • Meitu Inc.

Contact: [email protected]

License

You may use the codes and files for research only, including sharing and modifying the material. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.

Dataset and Annotation

Taxonomy

The COIN is organized in a hierarchical structure, which contains three levels: domain, task and step. The corresponding relationship can be found at taxonomy [link]. We provide the taxonomy file of COIN in csv format. Below, we show a small part of the texonomy stored in taxonomy.xlsx:

domain_target_mapping target_action_mapping
Domains Targets
... ...
Vehicle ChangeCarTire
Vehicle InstallLicensePlateFrame
... ...
Gadgets ReplaceCDDriveWithSSD
Target Id Target Label Action Id Action Label
... ... ... ...
13 ChangeCarTire 259 unscrew the screw
13 ChangeCarTire 260 jack up the car
13 ChangeCarTire 261 remove the tire
13 ChangeCarTire 262 put on the tire
13 ChangeCarTire 263 tighten the screws
... ... ... ...

We store the url of video and their annotation in JSON format, which can be accessed with the link [COIN](Project link page). The json file is similar to that of ActivityNet. Below, we show an example entry from the key field "database":

"LtRSn-ntcLY": {
			"duration": 131.0309,
			"class": "ReplaceCDDriveWithSSD",
			"video_url": "https://www.youtube.com/embed/LtRSn-ntcLY",
			"start": 56.640895694775196,
			"annotation": [
				{
					"id": "212",
					"segment": [
						60.0,
						69.0
					],
					"label": "take out the laptop CD drive"
				},
				{
					"id": "216",
					"segment": [
						71.0,
						82.0
					],
					"label": "insert the hard disk tray into the position of the CD drive"
				}
			],
			"subset": "training",
			"end": 85.714362947023,
			"recipe_type": 131
		}

From the entry, we can easily retrieve the Youtube ID, duration, ROI and procedure information of the video. The field "annotation" comprises of a list of all annotated procedures within the video. The field "class" and sub-field "id" correspond to "task" and "step" of the taxonomy respectively.

File Structure

The annotation information is saved in COIN.json.

Field Name Type Example Description
database string - Key filed of the annotation file.
- string LtRSn-ntcLY Youtube ID of the video.
duration float 56.640895694775196 Duration of the video in seconds.
class string ReplaceCDDriveWithSSD Name of the task in the video.
video_url string https://www.youtube.com/embed/LtRSn-ntcLY Url of the video.
start float 56.640895694775196 Start time of the ROI of the video.
end float 85.714362947023 End time of the ROI of the video.
subset string training or validation Subset of the video.
recipe_type int 131 ID number of the task.
annotation string - Annotation information of the video.
annotation:id int 212 ID number of the procedure.
annotation:label string take out the laptop CD drive Name of the procedure.
annotation:segment list of float (len=2) [60.0,69.0] Start and end time of the procedure.
Causal Influence Detection for Improving Efficiency in Reinforcement Learning

Causal Influence Detection for Improving Efficiency in Reinforcement Learning This repository contains the code release for the paper "Causal Influenc

Autonomous Learning Group 21 Nov 29, 2022
A real-time speech emotion recognition application using Scikit-learn and gradio

Speech-Emotion-Recognition-App A real-time speech emotion recognition application using Scikit-learn and gradio. Requirements librosa==0.6.3 numpy sou

Son Tran 6 Oct 04, 2022
NeuralTalk is a Python+numpy project for learning Multimodal Recurrent Neural Networks that describe images with sentences.

#NeuralTalk Warning: Deprecated. Hi there, this code is now quite old and inefficient, and now deprecated. I am leaving it on Github for educational p

Andrej 5.3k Jan 07, 2023
Official codebase for running the small, filtered-data GLIDE model from GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models.

GLIDE This is the official codebase for running the small, filtered-data GLIDE model from GLIDE: Towards Photorealistic Image Generation and Editing w

OpenAI 2.9k Jan 04, 2023
RaftMLP: How Much Can Be Done Without Attention and with Less Spatial Locality?

RaftMLP RaftMLP: How Much Can Be Done Without Attention and with Less Spatial Locality? By Yuki Tatsunami and Masato Taki (Rikkyo University) [arxiv]

Okojo 20 Aug 31, 2022
Learning RAW-to-sRGB Mappings with Inaccurately Aligned Supervision (ICCV 2021)

Learning RAW-to-sRGB Mappings with Inaccurately Aligned Supervision (ICCV 2021) PyTorch implementation of Learning RAW-to-sRGB Mappings with Inaccurat

Zhilu Zhang 53 Dec 20, 2022
Advantage Actor Critic (A2C): jax + flax implementation

Advantage Actor Critic (A2C): jax + flax implementation Current version supports only environments with continious action spaces and was tested on muj

Andrey 3 Jan 23, 2022
MT-GAN-PyTorch - PyTorch Implementation of Learning to Transfer: Unsupervised Domain Translation via Meta-Learning

MT-GAN-PyTorch PyTorch Implementation of AAAI-2020 Paper "Learning to Transfer: Unsupervised Domain Translation via Meta-Learning" Dependency: Python

29 Oct 19, 2022
TensorFlow Implementation of Unsupervised Cross-Domain Image Generation

Domain Transfer Network (DTN) TensorFlow implementation of Unsupervised Cross-Domain Image Generation. Requirements Python 2.7 TensorFlow 0.12 Pickle

Yunjey Choi 865 Nov 17, 2022
Clustergram - Visualization and diagnostics for cluster analysis in Python

Clustergram Visualization and diagnostics for cluster analysis Clustergram is a diagram proposed by Matthias Schonlau in his paper The clustergram: A

Martin Fleischmann 96 Dec 26, 2022
pq is a jq-like Pickle file viewer

pq PQ is a jq-like viewer/processing tool for pickle files. howto # pq '' file.pkl {'other': 456, 'test': 123} # pq 'table' file.pkl |other|test| | 45

3 Mar 15, 2022
This computer program provides a reference implementation of Lagrangian Monte Carlo in metric induced by the Monge patch

This computer program provides a reference implementation of Lagrangian Monte Carlo in metric induced by the Monge patch. The code was prepared to the final version of the accepted manuscript in AIST

Marcelo Hartmann 2 May 06, 2022
SpeechBrain is an open-source and all-in-one speech toolkit based on PyTorch.

The SpeechBrain Toolkit SpeechBrain is an open-source and all-in-one speech toolkit based on PyTorch. The goal is to create a single, flexible, and us

SpeechBrain 5.1k Jan 02, 2023
sktime companion package for deep learning based on TensorFlow

NOTE: sktime-dl is currently being updated to work correctly with sktime 0.6, and wwill be fully relaunched over the summer. The plan is Refactor and

sktime 573 Jan 05, 2023
Official code for "End-to-End Optimization of Scene Layout" -- including VAE, Diff Render, SPADE for colorization (CVPR 2020 Oral)

End-to-End Optimization of Scene Layout Code release for: End-to-End Optimization of Scene Layout CVPR 2020 (Oral) Project site, Bibtex For help conta

Andrew Luo 41 Dec 09, 2022
Diffusion Probabilistic Models for 3D Point Cloud Generation (CVPR 2021)

Diffusion Probabilistic Models for 3D Point Cloud Generation [Paper] [Code] The official code repository for our CVPR 2021 paper "Diffusion Probabilis

Shitong Luo 323 Jan 05, 2023
Official Tensorflow implementation of "M-LSD: Towards Light-weight and Real-time Line Segment Detection"

M-LSD: Towards Light-weight and Real-time Line Segment Detection Official Tensorflow implementation of "M-LSD: Towards Light-weight and Real-time Line

NAVER/LINE Vision 357 Jan 04, 2023
Iterative Normalization: Beyond Standardization towards Efficient Whitening

IterNorm Code for reproducing the results in the following paper: Iterative Normalization: Beyond Standardization towards Efficient Whitening Lei Huan

Lei Huang 21 Dec 27, 2022
Tensorflow implementation of Semi-supervised Sequence Learning (https://arxiv.org/abs/1511.01432)

Transfer Learning for Text Classification with Tensorflow Tensorflow implementation of Semi-supervised Sequence Learning(https://arxiv.org/abs/1511.01

DONGJUN LEE 82 Oct 22, 2022
catch-22: CAnonical Time-series CHaracteristics

catch22 - CAnonical Time-series CHaracteristics About catch22 is a collection of 22 time-series features coded in C that can be run from Python, R, Ma

Carl H Lubba 229 Oct 21, 2022