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KL-DIVERGENCE-BASED REGION PROPOSAL NETWORK FOR OBJECT DETECTION
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Seo, Geonseok | - |
dc.contributor.author | Yoo, Jaeyoung | - |
dc.contributor.author | Choi, Jaeseok | - |
dc.contributor.author | Kwak, Nojun | - |
dc.date.accessioned | 2024-08-08T01:25:41Z | - |
dc.date.available | 2024-08-08T01:25:41Z | - |
dc.date.created | 2022-10-14 | - |
dc.date.created | 2022-10-14 | - |
dc.date.issued | 2020-10 | - |
dc.identifier.citation | 2020 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), Vol.2020-October, pp.2001-2005 | - |
dc.identifier.issn | 1522-4880 | - |
dc.identifier.uri | https://hdl.handle.net/10371/205890 | - |
dc.description.abstract | The learning of the region proposal in object detection using the deep neural networks (DNN) is divided into two tasks: binary classification and bounding box regression task. However, traditional RPN (Region Proposal Network) defines these two tasks as different problems, and they are trained independently. In this paper, we propose a new region proposal learning method that considers the bounding box offset's uncertainty in the objectness score. Our method redefines RPN to a problem of minimizing the KL-divergence, difference between the two probability distributions. We applied KL-RPN, which performs region proposal using KL-Divergence, to the existing two-stage object detection framework and showed that it can improve the performance of the existing method. Experiments show that it achieves 2.6% and 2.0% AP improvements on MS COCO test-dev in Faster R-CNN with VGG-16 and R-FCN with ResNet-101 backbone, respectively. | - |
dc.language | 영어 | - |
dc.publisher | IEEE | - |
dc.title | KL-DIVERGENCE-BASED REGION PROPOSAL NETWORK FOR OBJECT DETECTION | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/ICIP40778.2020.9191075 | - |
dc.citation.journaltitle | 2020 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | - |
dc.identifier.wosid | 000646178502022 | - |
dc.identifier.scopusid | 2-s2.0-85098617347 | - |
dc.citation.endpage | 2005 | - |
dc.citation.startpage | 2001 | - |
dc.citation.volume | 2020-October | - |
dc.description.isOpenAccess | Y | - |
dc.contributor.affiliatedAuthor | Kwak, Nojun | - |
dc.type.docType | Proceedings Paper | - |
dc.description.journalClass | 1 | - |
dc.subject.keywordAuthor | Object Detection | - |
dc.subject.keywordAuthor | Neural Network | - |
dc.subject.keywordAuthor | Deep Learning | - |
dc.subject.keywordAuthor | KL-Divergence | - |
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