Hallucination In Object Detection -- A Study In Visual Part Verification

Understand

We show that object detectors can hallucinate and detect missing objects; potentially even accurately localized at their expected, but non-existing, position.

• This is particularly problematic for applications that rely on visual part verification: detecting if an object part is present or absent.
• We show how popular object detectors hallucinate objects in a visual part verification task and introduce the first visual part verification dataset: DelftBikes, which has 10,000 bike photographs, with 22 densely annotated parts per image, where some parts may be missing.
• We explicitly annotated an extra object state label for each part to reflect if a part is missing or intact.