Paper ID | ARS-1.11 | ||
Paper Title | HALLUCINATION IN OBJECT DETECTION — A STUDY IN VISUAL PART VERIFICATION | ||
Authors | Osman Semih Kayhan, Delft University of Technology, Netherlands; Bart Vredebregt, Aiir Innovations, Netherlands; Jan C. van Gemert, Delft University of Technology, Netherlands | ||
Session | ARS-1: Object Detection | ||
Location | Area I | ||
Session Time: | Tuesday, 21 September, 15:30 - 17:00 | ||
Presentation Time: | Tuesday, 21 September, 15:30 - 17:00 | ||
Presentation | Poster | ||
Topic | Image and Video Analysis, Synthesis, and Retrieval: Image & Video Mid-Level Analysis | ||
IEEE Xplore Open Preview | Click here to view in IEEE Xplore | ||
Abstract | 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: BikeParts, 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 BikeParts. |