Sign In

Communications of the ACM

Contributed articles

Unveiling Unexpected Training Data in Internet Video

multiple video screens, illustration

Credit: Getty Images

One of the most important components of training machine-learning models is data. The amount of training data, how clean it is, its diversity, how well it reflects the real world—all can have a dramatic effect on the performance of a trained model. Hence, collecting new datasets and finding reliable sources of supervision have become imperative for advancing the state of the art in many computer vision and graphics tasks, which have become highly dependent on machine learning. However, collecting such data at scale remains a fundamental challenge.

In this article, we focus on an intriguing source of training data—online videos. The Web hosts a large volume of video content, spanning an enormous space of real-world visual and auditory signals. The existence of such abundant data suggests the following question: How can we imbue machines with visual knowledge by directly observing the world through raw video? There are a number of challenges faced in exploring this question.


No entries found

Log in to Read the Full Article

Sign In

Sign in using your ACM Web Account username and password to access premium content if you are an ACM member, Communications subscriber or Digital Library subscriber.

Need Access?

Please select one of the options below for access to premium content and features.

Create a Web Account

If you are already an ACM member, Communications subscriber, or Digital Library subscriber, please set up a web account to access premium content on this site.

Join the ACM

Become a member to take full advantage of ACM's outstanding computing information resources, networking opportunities, and other benefits.

Subscribe to Communications of the ACM Magazine

Get full access to 50+ years of CACM content and receive the print version of the magazine monthly.

Purchase the Article

Non-members can purchase this article or a copy of the magazine in which it appears.
Sign In for Full Access
» Forgot Password? » Create an ACM Web Account