An initiative to bring the computer vision community closer to socially impactful tasks, datasets and applications for worldwide impact
1st Workshop on Computer Vision for Global Challenges @ CVPR 2019
June 16th 2019, Grand ballroom A, Long Beach Convention Center
The first Workshop on Computer Vision for Global Challenges will be held on June 16th, 2019, in conjunction with the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), in Long Beach, California. It will be a full-day event and will feature invited speakers, poster and spotlight presentations, a panel discussion and a mentoring/networking dinner.
We have selected 17 challenge winners to present their works, interact, inspire and get inspired by the computer vision community. We hope these challenges are not just applications that may affect billions, but also contribute to computer vision research by unveiling current limitations and biases. The selected challenge proposals come from 15 different countries and were selected among 100+ applications. From crop diseases to air pollution, from natural disaster prevention to wildlife poaching prevention, from dyslexia to malaria diagnosis, the challenges cover diverse areas like agriculture, health, education and meteorology, to name a few.
We have further invited a cohort of more than 15 International Development experts, making CV4GC the first workshop that tries to bridge the gap between the computer vision research, and international development communities.
Distribution of accepted challenge applications.
June 16th 2019, Grand ballroom A, Long Beach Convention Center
Links to all presented material will be added soon. Click on the button below to toggle session details.
Keynote Presentation - Collaboration and Co-Creation in Applying Computer Vision for Social
Impact Padmanabhan Anandan, CEO, Wadhwani AI
Talks and Discussion - 1 - Missing Pixels: The Need for Inclusion in AI Deborah Raji, Google AI
- Using satellite imagery to analyze spatial apartheid in South Africa Raesetje Sefala, Wits
talks from International Development Experts
- Creating labeled training datasets as a way to encourage native CV
applications in emerging markets. Sue Marques, Rockefeller Foundation
- Connected and digital health in a global perspective
Ben Pierson and Arunan Skandarajah, Bill and Melinda Gates Foundation
- Fighting counterfeiting of life-impacting products like medicines and agro-inputs, and securing cold-chains with CV in Africa, South Asia & Middle East
Bright Simons, Mpedigree
- Predicting deforestation and detecting illegal logging with CV in Chile
Micah Melnyk, World Bank/Singularity
- Disruptive tech for financial inclusion - or how to help East African shopkeepers manage their business?
Matt Grasser, Bankable Frontiers Associates
- Project Connect - how to map every school in the world without data
Zhuang-Fang Yi, Development Seed
Talks and Discussion - 2 - The role of context in the context of computer vision for development
challenges Ernest Mwebaze, Google AI Accra
- Misinformation: a looming threat to sustainable development in Latin
America. Claudia Flores Saviaga, West Virginia
Oral Presentations - Creating xBD: A Dataset for Assessing Building
Damage from Satellite Imagery Ritwik Gupta, Bryce Goodman, Nirav Patel, Ricky Hosfelt, Sandra Sajeev, Eric Heim, Jigar Doshi, Keane
Lucas, Howie Choset, Matthew Gaston
- Deep Landscape Features for Improving Vector-borne Disease Prediction
Nabeel Abdur Rehman, Umar Saif,
- Does Object Recognition Work for Everyone?
Terrance de Vries, Ishan Misra, Changhan
Wang, Laurens van der Maaten
- Infant-Prints: Fingerprints for Reducing Infant Mortality
Joshua Engelsma, Debayan
Deb, Anil Jain, Anjoo Bhatnagar, Prem Sewak Sudhish
Talks and Discussion - 3 - The use of computer vision in understanding and measuring human
well-being Marshall Burke (Stanford University)
- Title: TBD Jitendra Malik (UC Berkeley and Facebook AI)
Challenges - Lightning talks Health - A rapid malaria diagnosis test using a mobile phone
Martha Shaka (The University of Dodoma, Tanzania) - Early Detection and Diagnosis of Breast Cancer using Image Processing
and Machine Learning MaleikaHeenaye-Mamode Khan (University of Mauritius, Mauritius) - Towards Detecting Dyslexia in Handwriting Using Neural Networks
Katie Spoon (Indiana University Bloomington, USA) - "Bytes against bites": First AI-based app for snakebite
Rafael Ruiz de Castañeda (Institute of Global
Health, Faculty of Medicine, University of Geneva, Switzerland) - Computer Vision for Childbirth Progression Monitoring: Cervical Dilation Assessment
Temitope Oluwatosin Takpor (Covenant University,
Agriculture - Smartphone-based application for the identification of diseases in crops
using deep convolutional neural networks: AgroTIC +
SMART Ariolfo Camacho Velasco (Universidad Industrial de
Santander, Colombia) - CareGreenUnit and CareGreenMonitering
system: AI Support Decision Making System in Smart Agriculture Azeddine EL HASSOUNY (Mohammed V University in
Rabat, Morocco) - Intelligent Crop Health and Pest Detection
Mohsen Ali (Information Technology University, Pakistan) - A Computer Vision Tomato Pest Assessment and Prediction Tool
(Tokyo University of Agriculture, Japan) - Inferring Crop Pests and Diseases from Image Soil Data and Soil
Claire Babirye (Uganda Technology and Management
Remote Sensing - Eliminating Bonded Labour in Brick Kilns of
Murtaza Taj (Lahore University of Management Sciences, Pakistan) - Automatic Illegal mining detection using satellite imagery and computer
Leticia Leonor Pinto Alva (Universidad Catolica San
Pablo, Peru) - Poverty Prediction Using Satellite Imagery Chaiyaphum, Siripanpornchana
(NECTEC Thailand, Thailand) - Computer Vision for Drought Resilience
Andrew Hobbs (UC Davis, USA) - Storm Nowcasting Improvement by Machine Learning
Suzanna Maria Bonnet de Oliveira Martins (Federal University of Rio de Janeiro,
Wildlife & Air Quality - Automated Wildlife Monitoring, Tracking and Poacher Detection
(Dundee Precious Metals, Namibia) - Nowcasting Air Quality using Social Media Imagery
Anthony Mockler (UN Global Pulse Lab Jakarta,
Challenges - Breakout Session
and Coffee - Building High Resolution Maps for Humanitarian Aid and Development with
Weakly- and Semi-Supervised Learning
Derrick Bonafilia (Facebook); James Gill
(Facebook); David Yang (Facebook)
- Creating xBD: A Dataset for Assessing Building
Damage from Satellite Imagery Ritwik Gupta (Carnegie Mellon University Software
Engineering Institute); Bryce Goodman (Defense Innovation Unit); Nirav Patel
(Defense Innovation Unit); Ricky Hosfelt (Carnegie
Mellon University Software Engineering Institute); Sandra Sajeev (Carnegie
Mellon University Software Engineering Institute); Eric Heim (Carnegie Mellon
University Software Engineering Institute); Jigar Doshi (CrowdAI,
Inc.); Keane Lucas (Joint Artificial Intelligence Center); Howie Choset (Carnegie Mellon University); Matthew Gaston
(Carnegie Mellon University Software Engineering Institute)
- Weakly Labeling the Antarctic: The Penguin Colony Case Hieu Le (Stony Brook University); Bento Goncalves
(Stony Brook University); Dimitris Samaras (Stony Brook University); Heather
Lynch (Stony Brook University)
- Towards Autonomous Mining via Intelligent Excavators Hooman Shariati (Motion Metrics International); AnuarYeraliyev (Motion Metrics
International); Burhan Terai (Motion Metrics
International); Shahram Tafazoli (Motion Metrics
International); Mahdi Ramezani (Motion Metrics
- DisplaceNet: Recognising
Displaced People from Images by Exploiting Dominance Level GrigoriosKalliatakis
(University of Essex, UK); Shoaib Ehsan (University of Essex); Maria Fasli (University of Essex, UK); Klaus D McDonald-Maier
(University of Essex)
- Tiny-Inception-ResNet-v2: Using Deep Learning for Eliminating Bonded Labors
of Brick Kilns in South Asia
Usman Nazir (LUMS); Numan Khurshid (LUMS); Muhammad
Bhimra (LUMS); Murtaza Taj (LUMS) - Deep Landscape Features for Improving Vector-borne Disease Prediction
Nabeel Abdur Rehman (New York University); Umar Saif (United Nations - Pakistan); Rumi Chunara (New York University) - See the E-Waste! Training Visual Intelligence to See Dense Circuit
Boards for Recycling Ali Jahanian (MIT); Quang Le (MIT); Kamal Youcef-Toumi (MIT); DzmitryTsetserukou (skoltech) - Does Object Recognition Work for Everyone?
Terrance de Vries (University of Guelph); Ishan Misra
(Facebook AI Research); Changhan Wang (Facebook AI
Research); Laurens van der Maaten (Facebook)
- Towards equitable access to information and opportunity for all: mapping
schools with high-resolution Satellite Imagery and Machine Learning Zhuangfang Yi (Development Seed); NaroaZurutuza (UNICEF); Drew
Bollinger (Development Seed); Manuel Garcia-Herranz
(UNICEF); Dohyung Kim (UNICEF) - Infant-Prints: Fingerprints for Reducing Infant Mortality
Joshua Engelsma (Michigan State University); Debayan Deb (Michigan State University); Anil Jain
(Michigan State University); Anjoo Bhatnagar (Saran
Ashram Hospital); Prem Sewak Sudhish (Dayalbagh Engineering Institute) - Semantic Segmentation of Crop Type in Africa: A Novel Dataset and
Analysis of Deep Learning Methods
Rose Rustowicz (Stanford University); Robin Cheong
(Stanford); Lijing Wang (Stanford University);
Stefano Ermon (Stanford University); Marshall Burke
(Stanford University); David Lobell (Stanford University)
- Detecting Roads from Satellite Imagery in the Developing World
Yoni Nachmany (Radiant Earth Foundation); Hamed Alemohammad (Radiant Earth Foundation) - Predicting City Poverty Using Satellite Imagery
Simone Piaggesi (ISI Foundation / Bologna
University); Laetitia Gauvin (ISI); Michele Tizzoni
(ISI Foundation); Ciro Cattuto (ISI Foundation);
Natalia Adler (UNICEF); Stefaan Verhulst (The GovLab); Andrew Young (The GovLab);
Rhiannan Price (DigitalGlobe);
Leo Ferres (UDD); AndrèPanisson (ISI Foundation)
How Can These New Challenges Benefit Computer Vision?
- Jitendra Malik (UC Berkeley and Facebook AI)
- Timnit Gebru
- Peter Eckersley (Partnership on AI)
- Ernest Mwebaze (Google AI Accra)
- Laura Sevilla-Lara (University of Edinburgh)
Computer vision technology, a sub-class of Artificial Intelligence technologies, has recently made rapid progress in several subareas (e.g., object segmentation, video classification), achieving a level of performance that was unexpected just a few years ago. This technology has opened possibilities in many real world domains including transportation (self-driving cars, drones), entertainment (shopping, phones), safety (security systems), which are used on a daily basis to make our lives more efficient and safer. While these applications give meaning and value to our technology, their focus and presence in certain geographical regions or markets injects biases in the datasets, tasks, and ultimately direction of the advancement of the field.
However, increased global expansion of Internet and mobile devices (network connected computing devices) offers a unique opportunity for the computer vision community to both expand target markets and geographically diverse applications and input and training biases.
Thus, widening the scope of computer vision applications to address global problems could lead to a win for both the computer vision community and the organizations and individuals working to address global challenges: vision could positively impact the lives of 6 billion people in the Global South by developing applications for remote extension services for farming communities, digital health care delivery, or disaster readiness and mapping of informal settlements for example - and these populations could help reveal some of the blind spots and biases in the current computer vision datasets, tasks, and practices.
Why has this opportunity not been seized before? We argue that one of the main obstacles is the disconnection between domain experts: those who are close to the problems on the ground, and those who have knowledge about technical solutions. This disconnect might be driven by geographical divide, differences in language and taxonomy, or might come from the lack of a accessible forum to find each other. We propose this initiative as a first step to bridge the gap between these two communities. In particular our goals are:
To identify computer vision techniques that can help solve problems with large positive societal impact in the Global South
To give individuals, universities and organizations the ability to contribute to the previous two goals, through collaborations, mentorship, networking, research grants, etc.
Call for Challenges
Do you have an idea for a computer vision task that would impact the lives of many? Have you identified the limitations of a vision technique because of the geographical bias of the data you are using it on? Is there an application of computer vision that would be helpful to your community? Are you looking for potential vision expert partners or feedback on your idea?
Apply for the Call of Challenges, and come and experience the premier computer vision conference, and participate in an active discussion with the top vision researchers. Selected proposals will be presented as spotlight and/or posters during the workshop.
Recipients of accepted proposals may receive full travel grants (transportation, accommodation, registration and meals) to attend the CVPR conference, tutorials and workshops. The review process is single-blind and immigration letters will be provided.
Researchers or practitioners based in developing regions are strongly encouraged to apply.
Travel grants and US Visa: Keeping in mind how time-consuming the Visa processes can be, we expect to announce decisions for applicants that do not have a US Visa around mid-April. This will hopefully allow enough time for the visa process to finish on time.
The submission deadline for the Call for Challenges has now passed (was March 25th, 2019). We received 105 applications in total, and accepted 17 (16% acceptance rate!) applications from 15 countries!. The list of accepted applications will be out soon.
Call for papers
We invite researchers to submit their recent work on Computer Vision applications, tasks and challenges inspired by and applied to developing regions, including:
Applications of CV to development issues including health, education, institutional integrity, violence mitigation, economics, societal analysis, and environment.
Novel CV techniques inspired by limitations in development challenges.
Novel CV tasks and datasets that can potentially have large social impact in developing regions.
Limitations and risks of Computer Vision systems and applications for developing regions.
Paper presenters will be eligible for full (and partial) travel grants as well as oral presentations.
Researchers based in developing regions are strongly encouraged to submit.
Please note that, while there’s a wider range of work under the social good umbrella, we are particularly interested in works applied to and challenges coming from developing regions.
Papers should be submitted using the CVPR 2019 Latex/Word Templates and follow the CVPR formatting instructions. The review process is double-blind. The length must not exceed 6 pages (excluding references).
The Camera Ready deadline was May 8th (midnight PST), 2019.
Call for Research Proposals
In partnership with the CV4GC workshop, Facebook AI is calling for research proposals that extend computer vision technologies to achieve global development priorities, especially those captured in the UN Sustainable Development Goals.
Find more information about the call and apply through the Facebook AI website.
The deadline to apply has now passed.
Call for Computer Vision Ambassadors
Taking a global perspective on computer vision, also means widening participation. We will bring people who come to CVPR for the first time, which can be overwhelming (you probably still remember your first CVPR). We want these people to make the most of it, be prepared to showcase their material etc. We are therefore accepting volunteers from the CV community, that would act as "vision ambassadors" for each newcomer that needs help.
If accepted, you will be the mentor of a CVPR newcomer, and you are expected to connect with them before the conference, answer questions via email or video conferencing, assist them with their presentation and poster printing in needed. During the conference, you will help the newcomer orient, socialize and connect to the right people. We require that you have attended at least 2 top-tier Computer Vision conferences (CVPR/ICCV/ECCV) during the last couple of years to qualify.