Blog
The latest news from Google AI
Announcing the 7th Fine-Grained Visual Categorization Workshop
Wednesday, May 20, 2020
Posted by Christine Kaeser-Chen, Software Engineer and Serge Belongie, Visiting Faculty, Google Research
Fine-grained visual categorization refers to the problem of distinguishing between images of closely related entities, e.g., a monarch butterfly (
Danaus plexippus
) from a viceroy (
Limenitis archippus
). At the time of the first
FGVC
workshop in 2011, very few fine-grained datasets existed, and the ones that were available (e.g., the
CUB
dataset of 200 bird species, launched at that workshop) presented a formidable challenge to the leading classification algorithms of the time. Fast forward to 2020, and the computer vision landscape has undergone breathtaking changes. Deep learning based methods helped CUB-200-2011 accuracy rocket from 17% to 90% and fine-grained datasets have proliferated, with data arriving from a diverse array of institutions, such as art museums, apparel retailers, and cassava farms.
In order to help support even further progress in this field, we are excited to sponsor and co-organize the
7th Workshop on Fine-Grained Visual Categorization
(FGVC7), which will take place as a virtual gathering on June 19, 2020, in conjunction with the
IEEE
conference on
Computer Vision and Pattern Recognition
(CVPR). We’re excited to highlight this year’s world-class lineup of fine-grained challenges, ranging from fruit tree disease prediction to fashion attributes, and we invite computer vision researchers from across the world to participate in the workshop.
The FGVC workshop at CVPR 2020 focuses on subordinate categories, including (from left to right) wildlife camera traps, plant pathology, birds, herbarium sheets, apparel, and museum artifacts.
Real-World Impact of the FGVC Challenges
In addition to pushing the frontier of fine-grained recognition on ever more challenging datasets, each FGVC workshop cycle provides opportunities for fostering new collaborations between researchers and practitioners. Some of the efforts from the FGVC workshop have made the leap into the hands of real world users.
The 2018 FGVC workshop hosted a
Fungi challenge
with data for 1,500 mushroom species provided by the
Danish Mycological Society
. When the competition concluded, the leaderboard was topped by
a team from Czech Technical University and the University of West Bohemia
.
The mycologists subsequently invited the Czech researchers for a visit to Copenhagen to explore further collaboration and field test
a new workflow
for collaborative machine learning research in biodiversity. This resulted in a jointly authored
conference paper
, a mushroom recognition
app for Android
and
iOS
, and an
open access model
published on
TensorFlow Hub
.
The
Svampeatlas app
for mushroom recognition is a result of a Danish-Czech collaboration spun out of the FGVC 2018 Fungi challenge. The underlying model is now published on TF Hub. Images used with permission of the Danish Mycological Society.
The
iCassava Disease Challenge
from 2019 mentioned above is another example of an FGVC team effort finding its way into the real world. In this challenge, Google researchers in Ghana collaborated with
Makerere University
and the
National Crops Resources Research Institute
(NaCRRI) to produce an annotated dataset of five cassava disease categories.
Examples of cassava leaf disease represented in the 2019 iCassava challenge.
The teams are testing a new model in the fields in Uganda with local farmers, and the model will be published on TFHub soon.
This Year’s Challenges
FGVC7 will feature six challenges, four of which represent sequels to past offerings, and two of which are brand new.
In
iWildCam
, the challenge is to identify different species of animals in camera trap images. Like its predecessors in 2018 and 2019, this year’s competition makes use of data from static, motion-triggered cameras used by biologists to study animals in the wild. Participants compete to build models that address diverse regions from around the globe, with a focus on generalization to held-out camera deployments within those regions, which exhibit differences in device model, image quality, local environment, lighting conditions, and species distributions,
making generalization difficult
.
It has been shown that species classification performance can be dramatically improved by
using information
beyond
the image itself
. In addition, since an ecosystem can be monitored in a variety of ways (e.g., camera traps, citizen scientists, remote sensing), each of which has its own strengths and limitations, it is important to facilitate the exploration of techniques for combining these complementary modalities. To this end, the competition provides a time series of remote sensing imagery for each camera trap location, as well as images from the
iNaturalist competition datasets
for species in the camera trap data.
Side-by-side comparison of image quality from iWildcam, captured from wildlife camera traps, (
left
) and iNaturalist (
right
), captured by conventional cameras. Images are from the 2020 iWildCam Challenge, and the iNaturalist competition datasets from 2017 and 2018.
The
Herbarium Challenge
, now in its second year, entails plant species identification, based on a large, long-tailed collection of herbarium specimens. Developed in collaboration with the
New York Botanical Garden
(NYBG), this challenge features over 1 million images representing over 32,000 plant species. Last year’s challenge was based on 46,000 specimens for 680 species. Being able to recognize species from historical herbarium collections can not only help botanists better understand changes in plant life on our planet, but also offers a unique opportunity to identify previously undescribed new species in the collection.
Representative examples of specimens from the 2020 Herbarium challenge. Images used with permission of the New York Botanical Garden.
In this year’s
iMat Fashion
challenge, participants compete to perform apparel
instance segmentation
and fine-grained attribute classification. The goal of this competition is to push the state of the art in fine-grained segmentation by joining forces between the fashion and computer vision communities. This challenge is in its third iteration, growing both in size and level of detail over past years’ offerings.
The last of the sequels is
iMet
, in which participants are challenged with building algorithms for fine-grained attribute classification on works of art. Developed in collaboration with the
Metropolitan Museum of Art
, the dataset has grown significantly since the 2019 edition, with a wide array of new cataloguing information generated by subject matter experts including multiple object classifications, artist, title, period, date, medium, culture, size, provenance, geographic location, and other related museum objects within the Met’s collection.
Semi-Supervised Aves
is one of the new challenges at this year’s workshop. While avian data from
iNaturalist
has featured prominently in past FGVC challenges, this challenge focuses on the problem of learning from partially labeled data, a form of
semi-supervised learning
. The dataset is designed to expose some of the challenges encountered in realistic settings, such as the fine-grained similarity between classes, significant class imbalance, and domain mismatch between the labeled and unlabeled data.
Rounding out the set of challenges is
Plant Pathology
. In this challenge, the participants attempt to spot foliar diseases of apples using a reference dataset of expert-annotated diseased specimens. While this particular challenge is new to the FGVC community, it is the second such challenge to involve plant disease, the first being
iCassava
at last year’s FGVC.
Invitation to Participate
The results of these competitions will be presented at the FGVC7 workshop by top performing teams. We invite researchers, practitioners, and domain experts to participate in the FGVC workshop to learn more about state-of-the-art advances in fine-grained image recognition. We are excited to encourage the community's development of cutting edge algorithms for fine-grained visual categorization and foster new collaborations with global impact!
Acknowledgements
We’d like to thank our colleagues and friends on the FGVC7 organizing committee for working together to advance this important area. At Google we would like to thank Hartwig Adam, Kiat Chuan Tan, Arvi Gjoka, Kimberly Wilber, Sara Beery, Mikhail Sirotenko, Denis Brulé, Timnit Gebru, Ernest Mwebaze, Wojciech Sirko, Maggie Demkin.
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
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
.
