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Dynamic supervisor for cross-dataset object detection
- Source :
- Neurocomputing. 469:310-320
- Publication Year :
- 2022
- Publisher :
- Elsevier BV, 2022.
-
Abstract
- The application of cross-dataset training in object detection tasks is complicated because the inconsistency in the category range across datasets transforms fully supervised learning into semi-supervised learning. To address this problem, recent studies focus on the generation of high-quality missing annotations. In this study, we first specify that it is not enough to generate high-quality annotations using a single model, which looks only once for annotations. Through detailed experimental analyses, we further conclude that hard-label training is conducive for generating high-recall annotations, whereas soft-label training tends to obtain high-precision annotations. Inspired by the aspects mentioned above, we propose a dynamic supervisor framework that updates the annotations multiple times through multiple-updated submodels trained using hard and soft labels. In the final generated annotations, recall and precision improve significantly through the integration of hard-label training with soft-label training. Extensive experiments conducted on various dataset combination settings support our analyses and demonstrate the superior performance of the proposed dynamic supervisor.
- Subjects :
- FOS: Computer and information sciences
Single model
Supervisor
Computer science
business.industry
Computer Vision and Pattern Recognition (cs.CV)
Cognitive Neuroscience
Supervised learning
Computer Science - Computer Vision and Pattern Recognition
Machine learning
computer.software_genre
Object detection
Computer Science Applications
Range (mathematics)
Artificial Intelligence
Artificial intelligence
Focus (optics)
Precision and recall
business
computer
Subjects
Details
- ISSN :
- 09252312
- Volume :
- 469
- Database :
- OpenAIRE
- Journal :
- Neurocomputing
- Accession number :
- edsair.doi.dedup.....c08c0a61bebcacf12b1a386e2a469014
- Full Text :
- https://doi.org/10.1016/j.neucom.2021.09.076