CLOSING THE GAP: Teaching Computers to Reliably Identify Objects in Images using Large-Scale Unannotated Data
By Joshua Preston
Training computers to detect and reliably identify objects in images is a challenge even with advanced computing power. Humans need only see common objects a few times to learn what they are and the lesson sticks. Computer programs require massive annotated datasets, where humans draw boxes around all objects and identify them. One way to get around this is to train computers to use unlabeled data, by allowing the computer to make predictions of what is in the image (called “pseudo-labels”) and training on those predictions. If the computer gets the object classification wrong—such as labeling a walrus as a dog—the faulty data may be used in the future and the system risks becoming unreliable.
Zsolt Kira, assistant professor in Interactive Computing, and his team, including Ph.D. student Yen-Cheng Liu and collaborators at Meta, are working to train computer programs to more accurately classify objects in images and mitigate the risks of mislabeling data in large-scale real-world unlabeled datasets.
New research from the group is some of the first to explore how computer programs implement semi-supervised object detection or SSOD—using labeled data to apply the pseudo-labels to raw images that haven’t been labeled—for open datasets found on the internet.