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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.Ĭongratulations to the winners, runners-up, and all who participated! All prize-winning solutions have been released under an open source license for ongoing development and learning. As you'll see in the details to follow, SSL was a key component across all of the winning solutions. One goal of the competition sponsors at Facebook AI was to create an opportunity for participants to explore self-supervised learning (SSL) techniques in solving copy detection.
And for the Descriptor Track, participants were tasked with generating useful vector embeddings of the same query and reference images, such that related image pairs would receive high similarity scores. For the Matching Track, participants were tasked with identifying which query images were derived from a large corpus of reference images. In the months that followed, the DrivenData community delivered, and we're excited to now share the winning solutions!īetween June and October 2021, 1,236 participants from 80 countries signed up to solve the problems posed by the two tracks, and over 200 went on to submit solutions. Matthijs Douze, Facebook AI Research Scientist and Image Similarity Challenge authorīack in June 2021, we launched the Facebook AI Image Similarity Challenge, an ambitious computer vision competition with two tracks and two phases designed to elicit useful approaches to the problem of image copy detection. 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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