SPEAKER: Yong Jae Lee; Computer Science, UC Davis
TITLE: “Learning to localize and anonymize objects with indirect supervision"
ABSTRACT: Computer vision has made great strides for problems that can be learned with direct supervision, in which the goal can be precisely defined (e.g., drawing a box that tightly-fits an object). However, direct supervision is often not only costly, but also challenging to obtain when the goal is more ambiguous. In this talk, I will discuss our recent work on learning with indirect supervision. I will first present an approach that learns to focus on the relevant image regions given only indirect image-level supervision (e.g., an image tagged with "car"). This is enabled by a novel data augmentation technique that hides image patches randomly. I will then present an approach that learns to anonymize sensitive video regions while preserving activity signals in an adversarial framework. It accomplishes this by simultaneously optimizing for the indirectly-related task of misclassifying face identity and maximizing activity detection accuracy. We show that our anonymization method leads to superior performance compared to conventional hand-crafted anonymization methods including masking, blurring, and noise adding.
REFRESHMENTS: 3:30pm MSB 4110 (4th floor lounge)
STA 290 Seminar List: https://statistics.ucdavis.edu/seminars