![]() ![]() Winners must complete the required winner documentation in accordance with the Winning Model Documentation Template and publish their academic explanation of the Submission under a permissive license in order to be eligible for recognition and prize money. Winning Submissions and all source code used to generate the Submissions must be documented using the provided Winning Model Documentation Template which will require, without limitation, an academic explanation of the Submission and source code. To be eligible to win a prize in this Competition, winning Submissions and all source code used to generate the Submissions need to be made available under an Approved OSS License in order to be eligible for recognition and prize money. Winning License Type: Open licensing of winners Failure to satisfy these requirements or Sponsor’s belief that the Participant does not have all necessary consents and licenses (notwithstanding any certification by Participant) will result in disqualification from the Competition and forfeiture of any prize claim. Top performing Participants will be required to certify in writing that they have permission to use all External Data used to develop potentially winning Submissions, and may be required to provide documentation demonstrating such permission to Competition Sponsor’s satisfaction. External dataĮxternal Data (defined in the Competition Rules) is permitted in this Competition provided Participants have all rights, licenses and permissions to use the External Data as contemplated in the Competition Rules. The Competition will consist of two tracks (each, a “Track”), with one Track for Unconstrained Submissions and one for Constrained Submissions, as described in more detail in the Full Rules below. The start and end dates and times for each Phase are set forth on the Competition Website (collectively, the “Competition Period”). The Competition consists of a phase I (“Phase I”) and a phase II (“Phase II”) (where distinction is not necessary, each may be referred to as a “Phase” or together as “Phases”). Ultimately, the winning solutions vastly outperformed the competition baseline methods, achieving micro average precision scores of 0.8329 and 0.6354 on the Matching and Descriptor tracks, respectively. One goal of the competition sponsors at Facebook AI was to create an opportunity for participants to explore self-supervised learning (SSL) techniques, which turned out to be a key component across all of the winning solutions. The Resultsīetween June and October 2021, 1,236 participants from 80 countries signed up to solve the problems posed by the two tracks. The end goal was similar for the Descriptor Track, but in this case participants submitted the image embeddings for all query and reference images, with a similarity search and submission score computed automatically on the competition platform. The core task in the Matching Track was to determine for each query image whether it originated from one of the reference images and assign a confidence score indicating its similarity to the candidate reference image. 1 million training images, statistically similar to but distinct from the reference archive.50K query images, a subset of which were derived from the reference images.In this challenge, participants had access to 3 archives of competition images. For more information, check out the competition paper from Facebook AI. This competition allowed participants to test their skills in building a key part of that content moderating system, and in so doing contribute to making social media more trustworthy and safe for the people who use it. We need algorithms to help automatically flag or remove bad content. ![]() But when dealing with the billions of new images generated every day on sites like Facebook, manual content moderation just doesn't scale. Matthijs Douze, Facebook AI Research Scientist and Image Similarity Challenge author WhyĬopy detection is a crucial component on all social media platforms today, used for such tasks as flagging misinformation and manipulative advertising, preventing uploads of graphic violence, and enforcing copyright protections. ![]() The methods developed by the contestants are of high quality and set a new standard in research and for the industry in the field of image copy detection. ![]()
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