[December 2019]

Computer Vision for Agriculture (CV4A) Workshop @ ICLR 2020

We are excited to announce that the 1st workshop on Computer Vision for Agriculture (CV4A), the second workshop of the Computer Vision for Global Challenges initiative, will be held in April 2020 in conjunction with the International Conference on Representation Learning (ICLR), in Addis Ababa, Ethiopia. It will be a full-day event and will feature two open challenges with prizes, invited speakers, poster and spotlight presentations and panel discussions.

Find more info and important dates at the CV4A Workshop webpage.

[July 2019]

1st Workshop on Computer Vision for Global Challenges @ CVPR 2019

The inagural Workshop on Computer Vision for Global Challenges was 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.

Complete program and resources on the CV4GC CVPR 2019 Workshop webpage.


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:


Initiative Leads

Workshop co-Organizers

  • Ernest Mwebaze (Google AI Ghana)
  • Dina Machuve (Nelson Mandela African Institution of Science and Technology)
  • Lorenzo Torresani (Facebook AI)
  • Anna Lerner (Facebook Project 17)
  • Maria De-Arteaga (Carnegie Mellon University)
  • Kris Sankaran (MILA)
  • Julia Rhodes Davis (Partnership on AI)
  • Peter Eckersley (Partnership on AI)
  • Mourad Gridach (Ibn Zohr University, Agadir, Morocco)
  • Jamie Yang (Facebook)
  • Hamed Alemohammad (Radiant Earth Foundation)
  • David Guerena (CIMMYT)
  • Celina Lee (

Advisory Committee

  • Timnit Gebru (Google AI)
  • Mutembesa Daniel (Kampala University, Uganda)
  • Rebekkah Hogan (Facebook Academic Relations)
  • John Quinn (Makerere University & Google AI Ghana)
  • Padmanabhan Anandan (Wadhwani AI, India)
  • Stefano Ermon (Stanford University)
  • Amir Zamir (UC Berkeley & Stanford University)
  • Larry Zitnick (University of Washington & Facebook AI)
  • Jitendra Malik (UC Berkeley & Facebook AI)
  • Brian King (CGIAR)