Object Detection from Scratch with Deep Supervision
In this paper, we propose Deeply Supervised Object Detectors (DSOD), an object detection framework that can be trained from scratch. Recent advances in object detection heavily depend on the off-the-shelf models pre-trained on large-scale classification datasets like ImageNet and OpenImage. However,...
Ausführliche Beschreibung
Bibliographische Detailangaben
| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 42(2020), 2 vom: 14. Feb., Seite 398-412
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| 1. Verfasser: |
Shen, Zhiqiang
(VerfasserIn) |
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| Weitere Verfasser: |
Liu, Zhuang,
Li, Jianguo,
Jiang, Yu-Gang,
Chen, Yurong,
Xue, Xiangyang |
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| Format: | Online-Aufsatz
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| Sprache: | English |
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| Veröffentlicht: |
2020
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on pattern analysis and machine intelligence
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| Schlagworte: | Journal Article
Research Support, Non-U.S. Gov't |
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