Analysing object detectors from the perspective of co-occurring object categories
| Autor: | Sandor Jordan, Csaba Nemes |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Property (philosophy) Dependency (UML) business.industry Computer science Computer Vision and Pattern Recognition (cs.CV) Perspective (graphical) Computer Science - Computer Vision and Pattern Recognition Representation (systemics) Context (language use) 02 engineering and technology 010501 environmental sciences Object (computer science) computer.software_genre 01 natural sciences Object detection Knowledge-based systems 020204 information systems 0202 electrical engineering electronic engineering information engineering Artificial intelligence business computer Natural language processing 0105 earth and related environmental sciences |
| Zdroj: | 2018 9th IEEE International Conference on Cognitive Infocommunications (CogInfoCom). |
| Popis: | The accuracy of state-of-the-art Faster R-CNN and YOLO object detectors are evaluated and compared on a special masked MS COCO dataset to measure how much their predictions rely on contextual information encoded at object category level. Category level representation of context is motivated by the fact that it could be an adequate way to transfer knowledge between visual and non-visual domains. According to our measurements, current detectors usually do not build strong dependency on contextual information at category level, however, when they does, they does it in a similar way, suggesting that contextual dependence of object categories is an independent property that is relevant to be transferred. accepted to 9th IEEE International Conference on Cognitive InfoCommunications |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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