  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. We propose to
evaluate visual part verification by relying on recall and compare popular
object detectors on DelftBikes.

Computer Vision and Pattern Recognition;Artificial Intelligence;Machine Learning