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dc.contributor.author | Saveleva I. | |
dc.contributor.author | Razinkov E. | |
dc.date.accessioned | 2020-01-15T21:18:04Z | |
dc.date.available | 2020-01-15T21:18:04Z | |
dc.date.issued | 2019 | |
dc.identifier.issn | 0302-9743 | |
dc.identifier.uri | https://dspace.kpfu.ru/xmlui/handle/net/155601 | |
dc.description.abstract | © Springer Nature Switzerland AG 2019. The paper focuses on implementation details of ALFA – an agglomerative late fusion algorithm for object detection. ALFA agglomeratively clusters detector predictions while taking into account bounding box locations and class scores. We discuss the source code of ALFA and another late fusion algorithm – Dynamic Belief Fusion (DBF). The workflow and the hyperparameters necessary to reproduce the published results are presented. We also provide a framework for evaluation of late fusion algorithms like ALFA, DBF and Non-Maximum Suppression with arbitrary object detectors. | |
dc.relation.ispartofseries | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | |
dc.subject | Agglomerative clustering | |
dc.subject | Late fusion | |
dc.subject | Object detection | |
dc.title | On the Implementation of ALFA – Agglomerative Late Fusion Algorithm for Object Detection | |
dc.type | Conference Paper | |
dc.relation.ispartofseries-volume | 11455 LNCS | |
dc.collection | Публикации сотрудников КФУ | |
dc.relation.startpage | 98 | |
dc.source.id | SCOPUS03029743-2019-11455-SID85068981495 |