Hallucination In Object Detection -- A Study In Visual Part Verification
| Autor: | Bart Vredebregt, Jan C. van Gemert, Osman Semih Kayhan |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning business.industry Computer science Computer Science - Artificial Intelligence Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition Object (computer science) Object detection Machine Learning (cs.LG) Image (mathematics) Task (project management) Artificial Intelligence (cs.AI) Hallucinating Computer vision Artificial intelligence State (computer science) business |
| Popis: | 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. ICIP 2021 |
| Databáze: | OpenAIRE |
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