_{Credit: BrentOzar}

Overview

The ability to identify and track content on social media platforms, called content tracing, is crucial to the experience of billions of users on these platforms. Previously, Meta AI and DrivenData hosted the Image Similarity Challenge in which participants developed state-of-the-art models capable of accurately detecting when an image was derived from a known image. The motivation for detecting copies and manipulations with videos is similar — enforcing copyright protections, identifying misinformation, and removing violent or objectionable content.

Manual content moderation has challenges scaling to meet the large volume of content on platforms like Instagram and Facebook, where tens of thousands of hours of video are uploaded each day. Accurate and performant algorithms are critical in flagging and removing inappropriate content. This competition allows you to test your skills in building a key part of that content tracing system, and in so doing contribute to making social media more trustworthy and safe for the people who use it.

Open Arena

Though the prize-awarding portion of this competition is over, you may still explore the dataset for ongoing learning and research. This Open Arena provides you the opportunity to try your hand at each track of this challenge and see how you would have scored against the top competitors!

There are two tracks to this challenge:

• For the Descriptor Track, participants are tasked with generating useful vector embeddings for videos, up to one embedding per second of video, such that derived videos would receive high similarity scores to their corresponding reference video.

• For the Matching Track, participants are tasked with identifying the segments of a query video derived from corresponding segments of a reference video; Meta AI designed a segment-matching micro-average precision metric to measure performance on this Matching Track task.