What resources are needed to carry this out? GPU compute time?

@Harej, apologies for the delay, I was on holidays!

Resources will vary depending on the task. Given the heavy multimedia focus for this task, it is likely that participants will need GPU resources and substantial storage space to host the image datasets used for training. Participants might also need access to the Wikidata JSON dumps and the Wikipedia (and Commons?) XML dumps. I will get back to you with more details once we have figured out the specifics of the task.
Hope this helps!

Weekly updates:

• Estimated language and geographic distribution (thanks to Isaac's https://github.com/geohci/wiki-region-groundtruth Wiki Region Groundtruth data) of WIT test data
• Defined the legal constraints for image data publication.
• Worked with the WIT team to figure out next steps and involvement on their end, set up continuous communication channels and provided an detailed overview of timelines and commitments on our end.

Weekly updates:

• Met with the full team - including Google researchers and identified the next steps and deadlines. On our end, we will work full force on data release and on setting up the contract with the org responsible for setting up the challenge.
• Started process to generate the contract.
• Started process for data release.

Weekly updates:

• Progresses on data release, it was assessed as low risk by the security team pending a few checks on the images to be released.
• Progresses on the baseline design: tested the CLIP model on the WIT test data - the CLIP model has been trained on the same data, therefore it is difficult to assess the difficulty of the task. Next we will extend the notion of caption to "all surrounding text" and try to work on a more image-text retrieval baseline.

Weekly updates:

• Progresses on data release: working on polishing the list of images for public release.

Weekly updates:

• Progress on the data release as per T278217
• Progress on a multimodal-multilingual baseline based on a cross-modal network trained on WIT.

Weekly updates:

• Progress on the contract end
• No other updates as people away for holidays or other reasons

Weekly updates:

• Contract is signed
• Dataset in preparation
• We scoped the task as follows:

The task of this competition is  the following: given an image, retrieve the closest text from a large pool of words and sentences.
Images will come from Wikipedia articles, in many languages, and the target pieces of text will be taken from image captions and the title of the Wikipedia articles where images are placed.

Weekly updates:

• Competition launch is on hold due to discussion on the data nature and availability.
• We are putting together a workshop proposal for Neurips 2021 (deadline June 18th).

Weekly updates:

• We submitted a proposal for a NeurIPS 2021 workshop titled "Wiki-M3L: Wikipedia and Multimodal & Multilingual Research - How can the two communities help each other?" about using Wikimedia data for multimodal ML, and using multimodal ML technologies to serve the community needs. The competition-related papers and awards are part of our workshop program.
• We agreed on a playground competition. We are preparing data and details so that we can start running the competition in August.