Cross-Supervised Object Detection

Understand

After learning a new object category from image-level annotations (with no object bounding boxes), humans are remarkably good at precisely localizing those objects.

• However, building good object localizers (i.e., detectors) currently requires expensive instance-level annotations.
• While some work has been done on learning detectors from weakly labeled samples (with only class labels), these detectors do poorly at localization.
• In this work, we show how to build better object detectors from weakly labeled images of new categories by leveraging knowledge learned from fully labeled base categories.