Blog
The latest news from Google AI
An updated YouTube-8M, a video understanding challenge, and a CVPR workshop. Oh my!
Wednesday, February 15, 2017
Posted by Paul Natsev, Software Engineer
Last September, we released the
YouTube-8M dataset
, which spans
millions of videos labeled with thousands of classes
, in order to spur innovation and advancement in large-scale video understanding. More recently, other teams at Google have released datasets such as
Open Images
and
YouTube-BoundingBoxes
that, along with YouTube-8M, can be used to accelerate image and video understanding. To further these goals, today we are releasing an update to the
YouTube-8M dataset
, and in collaboration with
Google Cloud Machine Learning
and
kaggle.com
, we are also organizing a
video understanding competition
and an affiliated
CVPR’17 Workshop
.
An Updated YouTube-8M
The new and improved YouTube-8M includes cleaner and more verbose labels (twice as many labels per video, on average), a cleaned-up set of videos, and for the first time, the dataset includes pre-computed audio features, based on a state-of-the-art
audio modeling architecture
, in addition to the previously released visual features. The audio and visual features are synchronized in time, at 1-second temporal granularity, which makes YouTube-8M a large-scale multi-modal dataset, and opens up opportunities for exciting new research on joint audio-visual (temporal) modeling. Key statistics on the new version are illustrated below (more details
here
).
A tree-map visualization of the updated YouTube-8M dataset, organized into 24 high-level verticals, including the top-200 most frequent entities, plus the top-5 entities for each vertical.
Sample videos from the top-18 high-level verticals in the YouTube-8M dataset.
The Google Cloud & YouTube-8M Video Understanding Challenge
We are also excited to announce the
Google Cloud & YouTube-8M Video Understanding Challenge
, in partnership with
Google Cloud
and
kaggle.com
. The challenge invites participants to build audio-visual content classification models using YouTube-8M as training data, and to then label ~700K unseen test videos. It will be hosted as a
Kaggle competition
, sponsored by Google Cloud, and will feature a $100,000 prize pool for the top performers (details
here
). In order to enable wider participation in the competition, Google Cloud is also offering credits so participants can optionally do model training and exploration using
Google Cloud Machine Learning
. Open-source TensorFlow code, implementing a few baseline classification models for YouTube-8M, along with training and evaluation scripts, is available at
Github
. For details on getting started with local or cloud-based training, please see our
README
and the
getting started guide on Kaggle
.
The CVPR 2017 Workshop on YouTube-8M Large-Scale Video Understanding
We will announce the results of the challenge and host invited talks by distinguished researchers at the
1st YouTube-8M Workshop
, to be held July 26, 2017, at the 30th IEEE Conference on Computer Vision and Pattern Recognition (
CVPR 2017
) in Honolulu, Hawaii. The workshop will also feature presentations by top-performing challenge participants and a selected set of paper submissions. We
invite
researchers to submit papers describing novel research, experiments, or applications based on YouTube-8M dataset, including papers summarizing their participation in the above challenge.
We designed this dataset with scale and diversity in mind, and hope lessons learned here will generalize to many video domains (YouTube-8M captures over 20 diverse video domains). We believe the challenge can also accelerate research by enabling researchers without access to big data or compute clusters to explore and innovate at previously unprecedented scale. Please join us in advancing video understanding!
Acknowledgements
This post reflects the work of many others within Machine Perception at Google Research, including Sami Abu-El-Haija, Anja Hauth, Nisarg Kothari, Joonseok Lee, Hanhan Li, Sobhan Naderi Parizi, Rahul Sukthankar, George Toderici, Balakrishnan Varadarajan, Sudheendra Vijayanarasimhan, Jiang Wang, as well as Philippe Poutonnet and Mike Styer from Google Cloud, and our partners at Kaggle. We are grateful for the support and advice from many others at Google Research, Google Cloud, and YouTube, and especially thank Aren Jansen, Jort Gemmeke, Dan Ellis, and the Google Research Sound Understanding team for providing the audio features in the updated dataset.
Labels
2018
accessibility
ACL
ACM
Acoustic Modeling
Adaptive Data Analysis
ads
adsense
adwords
Africa
AI
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
Reinforcement Learning
renewable energy
Research
Research Awards
resource optimization
Robotics
schema.org
Search
search ads
Security and Privacy
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
2019
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
.
