Blog
The latest from Google Research
Announcing an updated YouTube-8M, and the 2nd YouTube-8M Large-Scale Video Understanding Challenge and Workshop
Tuesday, June 5, 2018
Posted by Joonseok Lee, Software Engineer, Google AI
Last year, we organized the first
YouTube-8M Large-Scale Video Understanding Challenge
with
Kaggle
, in which 742 teams consisting of 946 individuals from 60 countries used the
YouTube-8M dataset
(2017 edition) to develop classification algorithms which accurately assign video-level labels. The purpose of the competition was to accelerate improvements in large-scale video understanding, representation learning, noisy data modeling, transfer learning and domain adaptation approaches that can help improve the machine-learning models that classify video. In addition to the competition, we hosted an affiliated
workshop at CVPR’17
, inviting competition top-performers and researchers and share their ideas on how to advance the state-of-the-art in video understanding.
As a continuation of these efforts to accelerate video understanding, we are excited to announce another
update to the YouTube-8M dataset
,
a new Kaggle video understanding challenge
and an affiliated
2nd Workshop on YouTube-8M Large-Scale Video Understanding
, to be held at the
2018 European Conference on Computer Vision
(ECCV'18).
An Updated YouTube-8M Dataset (2018 Edition)
Our YouTube-8M (2018 edition) features a major improvement in the quality of annotations, obtained using a machine learning system that combines audio-visual content with title, description and other metadata to provide more accurate
ground truth
annotations. The updated version contains 6.1 million URLs, labeled with a vocabulary of 3,862 visual entities, with each video annotated with one or more labels and an average of 3 labels per video. We have also updated the
starter code
, with updated instructions for downloading and training
TensorFlow
video annotation models on the dataset.
The 2nd YouTube-8M Video Understanding Challenge
The
2nd YouTube-8M Video Understanding Challenge
invites participants to build audio-visual content classification models using YouTube-8M as training data, and then to label an unknown subset of test videos. Unlike last year, we strictly impose a hard limit on model size, encouraging participants to advance a single model within tight budget rather than assembling as many models as possible. Each of the top 5 teams will be awarded $5,000 to support their travel to Munich to attend ECCV’18. For details, please visit the
Kaggle competition page
.
The 2nd Workshop on YouTube-8M Large-Scale Video Understanding
To be held at
ECCV’18
, the workshop will consist of invited talks by distinguished researchers, as well as presentations by top-performing challenge participants in order to facilitate the exchange of ideas. We encourage those who wish to attend to submit papers describing their research, experiments, or applications based on YouTube-8M dataset, including papers summarizing their participation in the challenge above. Please refer to the
workshop page
for more details.
It is our hope that this update to the dataset, along with the new challenge and workshop, will continue to advance the research in large-scale video understanding. We hope you will join us again!
Acknowledgements
This post reflects the work of many machine perception researchers including Sami Abu-El-Haija, Ke Chen, Nisarg Kothari, Joonseok Lee, Hanhan Li, Paul Natsev, Sobhan Naderi Parizi, Rahul Sukthankar, George Toderici, Balakrishnan Varadarajan, as well as Sohier Dane, Julia Elliott, Wendy Kan and Walter Reade from Kaggle. We are also grateful for the support and advice from our partners at YouTube.
Labels
accessibility
ACL
ACM
Acoustic Modeling
Adaptive Data Analysis
ads
adsense
adwords
Africa
AI
AI for Social Good
Algorithms
Android
Android Wear
API
App Engine
App Inventor
April Fools
Art
Audio
Augmented Reality
Australia
Automatic Speech Recognition
AutoML
Awards
BigQuery
Cantonese
Chemistry
China
Chrome
Cloud Computing
Collaboration
Compression
Computational Imaging
Computational Photography
Computer Science
Computer Vision
conference
conferences
Conservation
correlate
Course Builder
crowd-sourcing
CVPR
Data Center
Data Discovery
data science
datasets
Deep Learning
DeepDream
DeepMind
distributed systems
Diversity
Earth Engine
economics
Education
Electronic Commerce and Algorithms
electronics
EMEA
EMNLP
Encryption
entities
Entity Salience
Environment
Europe
Exacycle
Expander
Faculty Institute
Faculty Summit
Flu Trends
Fusion Tables
gamification
Gboard
Gmail
Google Accelerated Science
Google Books
Google Brain
Google Cloud Platform
Google Docs
Google Drive
Google Genomics
Google Maps
Google Photos
Google Play Apps
Google Science Fair
Google Sheets
Google Translate
Google Trips
Google Voice Search
Google+
Government
grants
Graph
Graph Mining
Hardware
HCI
Health
High Dynamic Range Imaging
ICCV
ICLR
ICML
ICSE
Image Annotation
Image Classification
Image Processing
Inbox
India
Information Retrieval
internationalization
Internet of Things
Interspeech
IPython
Journalism
jsm
jsm2011
K-12
Kaggle
KDD
Keyboard Input
Klingon
Korean
Labs
Linear Optimization
localization
Low-Light Photography
Machine Hearing
Machine Intelligence
Machine Learning
Machine Perception
Machine Translation
Magenta
MapReduce
market algorithms
Market Research
Mixed Reality
ML
ML Fairness
MOOC
Moore's Law
Multimodal Learning
NAACL
Natural Language Processing
Natural Language Understanding
Network Management
Networks
Neural Networks
NeurIPS
Nexus
Ngram
NIPS
NLP
On-device Learning
open source
operating systems
Optical Character Recognition
optimization
osdi
osdi10
patents
Peer Review
ph.d. fellowship
PhD Fellowship
PhotoScan
Physics
PiLab
Pixel
Policy
Professional Development
Proposals
Public Data Explorer
publication
Publications
Quantum AI
Quantum Computing
Recommender Systems
Reinforcement Learning
renewable energy
Research
Research Awards
resource optimization
Robotics
schema.org
Search
search ads
Security and Privacy
Self-Supervised Learning
Semantic Models
Semi-supervised Learning
SIGCOMM
SIGMOD
Site Reliability Engineering
Social Networks
Software
Sound Search
Speech
Speech Recognition
statistics
Structured Data
Style Transfer
Supervised Learning
Systems
TensorBoard
TensorFlow
TPU
Translate
trends
TTS
TV
UI
University Relations
UNIX
Unsupervised Learning
User Experience
video
Video Analysis
Virtual Reality
Vision Research
Visiting Faculty
Visualization
VLDB
Voice Search
Wiki
wikipedia
WWW
Year in Review
YouTube
Archive
2021
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2020
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2019
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2018
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2017
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2016
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2015
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2014
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2013
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2012
Dec
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2011
Dec
Nov
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2010
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2009
Dec
Nov
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2008
Dec
Nov
Oct
Sep
Jul
May
Apr
Mar
Feb
2007
Oct
Sep
Aug
Jul
Jun
Feb
2006
Dec
Nov
Sep
Aug
Jul
Jun
Apr
Mar
Feb
Feed
Follow @googleai
Give us feedback in our
Product Forums
.
